Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives
Countries citing papers authored by Dominique Lord
Since
Specialization
Citations
This map shows the geographic impact of Dominique Lord'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 Dominique Lord with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dominique Lord more than expected).
This network shows the impact of papers produced by Dominique Lord. 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 Dominique Lord. The network helps show where Dominique Lord may publish in the future.
Co-authorship network of co-authors of Dominique Lord
This figure shows the co-authorship network connecting the top 25 collaborators of Dominique Lord.
A scholar is included among the top collaborators of Dominique Lord 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 Dominique Lord. Dominique Lord is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Flannagan, Carol, et al.. (2018). Using Crash Prediction Models with Economic Factors for State Highway Safety Planning. Transportation Research Board 97th Annual MeetingTransportation Research Board.1 indexed citations
11.
Mitra, Sudeshna, Srinivas Reddy Geedipally, & Dominique Lord. (2017). Safety Analysis of Urban Signalized Intersections in Kolkata, India Using a Combined Proactive and Reactive Approach. Transportation Research Board 96th Annual MeetingTransportation Research Board.5 indexed citations
12.
Zeng, Xiaosi, et al.. (2012). Guidelines for Choosing Hot-Spot Analysis Tools Based on Data Characteristics, Network Restrictions, and Time Distributions. Transportation Research Board 91st Annual MeetingTransportation Research Board.17 indexed citations
13.
Lord, Dominique, et al.. (2012). Over- and Under-Dispersed Count Data: Comparing Conway-Maxwell-Poisson and Double-Poisson Distributions. Transportation Research Board 91st Annual MeetingTransportation Research Board.7 indexed citations
14.
Geedipally, Srinivas Reddy, et al.. (2012). Examining the Poisson-Weibull Generalized Linear Model for Analyzing Crash Data. Transportation Research Board 91st Annual MeetingTransportation Research Board.3 indexed citations
15.
Park, Byung-Jung, et al.. (2010). Bias Properties of Bayesian Statistics in Finite Mixture of Negative Binomial Regression Models. Transportation Research Board 89th Annual MeetingTransportation Research Board.1 indexed citations
Brewer, Marcus A., et al.. (2005). Motorist Compliance to Staged Pedestrians for Pedestrian Treatments at Unsignalized Intersections.1 indexed citations
19.
Lord, Dominique, et al.. (1999). SAFETY ISSUES IN DYNAMIC ROUTE GUIDANCE.3 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.