Shaw‐Pin Miaou

3.3k total citations · 1 hit paper
32 papers, 2.6k citations indexed

About

Shaw‐Pin Miaou is a scholar working on Safety, Risk, Reliability and Quality, Civil and Structural Engineering and Building and Construction. According to data from OpenAlex, Shaw‐Pin Miaou has authored 32 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Safety, Risk, Reliability and Quality, 15 papers in Civil and Structural Engineering and 11 papers in Building and Construction. Recurrent topics in Shaw‐Pin Miaou's work include Traffic and Road Safety (20 papers), Infrastructure Maintenance and Monitoring (10 papers) and Traffic Prediction and Management Techniques (9 papers). Shaw‐Pin Miaou is often cited by papers focused on Traffic and Road Safety (20 papers), Infrastructure Maintenance and Monitoring (10 papers) and Traffic Prediction and Management Techniques (9 papers). Shaw‐Pin Miaou collaborates with scholars based in United States. Shaw‐Pin Miaou's co-authors include H S Lum, Dominique Lord, Joon Jin Song, David R. Maidment, Bani K. Mallick, Melba M. Crawford, Malay Ghosh, Roger P Bligh, Stacy Cagle Davis and Ajay K. Rathi and has published in prestigious journals such as Water Resources Research, European Journal of Operational Research and Accident Analysis & Prevention.

In The Last Decade

Shaw‐Pin Miaou

29 papers receiving 2.3k citations

Hit Papers

The relationship between truck accidents and geometric de... 1994 2026 2004 2015 1994 200 400 600

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shaw‐Pin Miaou United States 19 2.0k 807 723 670 434 32 2.6k
Charles V. Zegeer United States 29 2.3k 1.1× 1.3k 1.6× 530 0.7× 446 0.7× 610 1.4× 139 2.9k
Luis Miranda-Moreno Canada 47 3.3k 1.6× 3.5k 4.3× 528 0.7× 1.2k 1.7× 908 2.1× 204 5.9k
D W Harwood United States 28 2.2k 1.1× 645 0.8× 969 1.3× 667 1.0× 264 0.6× 153 2.8k
Albert Gan United States 23 1.1k 0.6× 1.1k 1.3× 284 0.4× 684 1.0× 306 0.7× 114 1.9k
Khaled Ksaibati United States 26 1.4k 0.7× 497 0.6× 1.4k 2.0× 693 1.0× 398 0.9× 277 2.9k
John N. Ivan United States 25 2.4k 1.2× 1.3k 1.6× 597 0.8× 718 1.1× 743 1.7× 103 2.9k
Aemal J. Khattak United States 21 1.1k 0.6× 546 0.7× 283 0.4× 520 0.8× 335 0.8× 91 1.6k
Ezra Hauer Canada 33 4.2k 2.1× 1.4k 1.7× 1.5k 2.0× 1.3k 1.9× 707 1.6× 113 4.8k
Eun Sug Park United States 24 816 0.4× 375 0.5× 579 0.8× 303 0.5× 138 0.3× 135 1.9k
Xiao Qin United States 26 1.5k 0.8× 1.1k 1.4× 324 0.4× 706 1.1× 365 0.8× 144 2.5k

Countries citing papers authored by Shaw‐Pin Miaou

Since Specialization
Citations

This map shows the geographic impact of Shaw‐Pin Miaou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shaw‐Pin Miaou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shaw‐Pin Miaou more than expected).

Fields of papers citing papers by Shaw‐Pin Miaou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shaw‐Pin Miaou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shaw‐Pin Miaou. The network helps show where Shaw‐Pin Miaou may publish in the future.

Co-authorship network of co-authors of Shaw‐Pin Miaou

This figure shows the co-authorship network connecting the top 25 collaborators of Shaw‐Pin Miaou. A scholar is included among the top collaborators of Shaw‐Pin Miaou based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shaw‐Pin Miaou. Shaw‐Pin Miaou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ray, Malcolm H., et al.. (2022). Roadside Safety Analysis Program (RSAP) Update. Transportation Research Board eBooks. 3 indexed citations
2.
Miaou, Shaw‐Pin. (2013). Some Limitations of the Models in the Highway Safety Manual to Predict Run-off-Road Crashes. Transportation Research Record Journal of the Transportation Research Board. 2377(1). 38–48. 1 indexed citations
3.
Miaou, Shaw‐Pin & Joon Jin Song. (2005). Bayesian ranking of sites for engineering safety improvements: Decision parameter, treatability concept, statistical criterion, and spatial dependence. Accident Analysis & Prevention. 37(4). 699–720. 191 indexed citations
4.
Song, Joon Jin, Malay Ghosh, Shaw‐Pin Miaou, & Bani K. Mallick. (2005). Bayesian multivariate spatial models for roadway traffic crash mapping. Journal of Multivariate Analysis. 97(1). 246–273. 120 indexed citations
5.
Bligh, Roger P, et al.. (2004). Developing an In-Service Performance Evaluation (ISPE) for Roadside Safety Features in Texas.
6.
Miaou, Shaw‐Pin, Joon Jin Song, & Bani K. Mallick. (2003). ROADWAY TRAFFIC CRASH MAPPING: A SPACE-TIME MODELING APPROACH. 6(1). 147 indexed citations
7.
Miaou, Shaw‐Pin. (2001). TRACKING CRASHES ON OUR HIGHWAYS. 37(3). 2 indexed citations
8.
Miaou, Shaw‐Pin. (1997). Estimating Vehicle Roadside Encroachment Frequencies by Using Accident Prediction Models. Transportation Research Record Journal of the Transportation Research Board. 1599(1). 64–71. 12 indexed citations
9.
Miaou, Shaw‐Pin, et al.. (1996). Pitfalls of Using R2 to Evaluate Goodness of Fit of Accident Prediction Models. Transportation Research Record Journal of the Transportation Research Board. 1542(1). 6–13. 32 indexed citations
10.
Miaou, Shaw‐Pin. (1996). Estimating vehicle roadside encroachment frequency using accident prediction models. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).
11.
Miaou, Shaw‐Pin, et al.. (1996). Pitfalls of UsingR2 To Evaluate Goodness of Fit of Accident Prediction Models. Transportation Research Record Journal of the Transportation Research Board. 1542. 6–13. 43 indexed citations
12.
Miaou, Shaw‐Pin. (1995). FACTORS ASSOCIATED WITH AGGREGATE CAR SCRAPPAGE RATE IN THE UNITED STATES: 1966-1992. Transportation Research Record Journal of the Transportation Research Board. 3–9. 4 indexed citations
13.
Miaou, Shaw‐Pin. (1994). The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident Analysis & Prevention. 26(4). 471–482. 623 indexed citations breakdown →
14.
Miaou, Shaw‐Pin & H S Lum. (1993). STATISTICAL EVALUATION OF THE EFFECTS OF HIGHWAY GEOMETRIC DESIGN ON TRUCK ACCIDENT INVOLVEMENTS. Transportation Research Record Journal of the Transportation Research Board. 48 indexed citations
15.
Miaou, Shaw‐Pin & H S Lum. (1993). Modeling vehicle accidents and highway geometric design relationships. Accident Analysis & Prevention. 25(6). 689–709. 422 indexed citations
16.
Miaou, Shaw‐Pin, et al.. (1993). Development of Relationship Between Truck Accidents and Geometric Design: Phase I. 19 indexed citations
17.
Miaou, Shaw‐Pin, Patricia S. Hu, Tommy Wright, Ajay K. Rathi, & Stacy Cagle Davis. (1992). RELATIONSHIP BETWEEN TRUCK ACCIDENTS AND HIGHWAY GEOMETRIC DESIGN: A POISSON REGRESSION APPROACH. Transportation Research Record Journal of the Transportation Research Board. 81 indexed citations
18.
Chin, Sang, et al.. (1992). Transportation demand forecasting with a computer-simulated neural network model. 10 indexed citations
19.
Miaou, Shaw‐Pin & Shih-Miao Chin. (1991). Computing k-shortest path for nuclear spent fuel highway transportation. European Journal of Operational Research. 53(1). 64–80. 16 indexed citations
20.
Miaou, Shaw‐Pin. (1990). A stepwise time series regression procedure for water demand model identification. Water Resources Research. 26(9). 1887–1897. 19 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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