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.
Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application
2019546 citationsStephane Hess et al.Journal of Choice Modellingprofile →
Handbook of Choice Modelling
2014350 citationsStephane Hess, Andrew Dalyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Stephane Hess'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 Stephane Hess with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephane Hess more than expected).
This network shows the impact of papers produced by Stephane Hess. 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 Stephane Hess. The network helps show where Stephane Hess may publish in the future.
Co-authorship network of co-authors of Stephane Hess
This figure shows the co-authorship network connecting the top 25 collaborators of Stephane Hess.
A scholar is included among the top collaborators of Stephane Hess 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 Stephane Hess. Stephane Hess is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Paschalidis, Evangelos, Charisma F. Choudhury, & Stephane Hess. (2019). Modelling the effects of stress in the car-following model using driving simulator and physiological sensor data. Transportation Research Board 98th Annual MeetingTransportation Research Board.1 indexed citations
Hess, Stephane, et al.. (2017). Can a better model specification avoid the need to move away from random utility maximisation. Transportation Research Board 96th Annual MeetingTransportation Research Board.2 indexed citations
Hess, Stephane, et al.. (2016). How much do attitudes and values matter in mode choice. Transportation Research Board 95th Annual MeetingTransportation Research Board.1 indexed citations
Liu, Ronghui, et al.. (2014). A Bayesian Modelling Framework for Individual Passenger’s Probabilistic Route Choices: A Case Study on the London Underground. White Rose Research Online (University of Leeds, The University of Sheffield, University of York).9 indexed citations
13.
Hess, Stephane, Matthew J. Beck, & Caspar Chorus. (2014). Contrasts Between Utility Maximization and Regret Minimization in the Presence of Opt Out Alternatives. Transportation Research Board 93rd Annual MeetingTransportation Research Board.2 indexed citations
14.
Hess, Stephane, et al.. (2013). A latent variable approach to dealing with missing or inaccurately measured variables: the case of income.1 indexed citations
Hess, Stephane, et al.. (2009). Using Second Preference Choices in Pivot Surveys as a Means of Dealing with Inertia.1 indexed citations
17.
Hess, Stephane, et al.. (2009). Random Scale Heterogeneity in Discrete Choice Models. Transportation Research Board 89th Annual MeetingTransportation Research Board.5 indexed citations
18.
Hess, Stephane & Andrew Daly. (2009). Calculating Errors for Measures Derived from Choice Modeling Estimates. White Rose Research Online (University of Leeds, The University of Sheffield, University of York).4 indexed citations
19.
Bliemer, Michiel C.J., John M. Rose, & Stephane Hess. (2007). Approximation of Bayesian Efficiency in Experimental Choice Designs. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 1–26.3 indexed citations
20.
Hess, Stephane, John M. Rose, & David A. Hensher. (2006). Asymmetrical Preference Formation in Willingness to Pay Estimates in Discrete Choice Models. The Sydney eScholarship Repository (The University of Sydney).16 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.