Dan Graham is Professor of Statistical Modelling in the Department of Civil and Environmental Engineering at Imperial College London (ICL) and Director of the ICL Transport Strategy Centre (TSC). He holds doctoral degrees from the Department of Mathematics at Imperial and from the London School of Economics.
Dan's research group works on mathematical and statistical models with applications in transportation. Key research themes include: statistical modelling for performance analytics; causal inference methods and applications; data centric engineering; mathematical modelling in economics, operations and planning; wider economic impacts; and resilience, risk and safety analyses. He has published extensively in these fields and he holds citation records for a number of his papers.
Dan was appointed Specialist Advisor to the UK Parliamentary Select Committee on Transport and he provides public policy advice internationally.
He is Deputy Head and Director of Teaching in the Transport Engineering Section, with overall responsibility for intercollegiate Msc Degrees.
He is a Project Partner of the Data Centric Engineering Program at the Alan Turing Institute, a Fellow of the Institute of Mathematics and its Applications, and a visiting Professor at South East University in Nanjing.
Singh R, Graham DJ, Anderson RJ, Quantifying the effects of passenger-level heterogeneity on transit journey times, Data-centric Engineering, ISSN:2632-6736
et al., 2019, Do speed cameras reduce road traffic collisions?, PLOS One, Vol:14, ISSN:1932-6203
Graham D, Gibbons S, 2019, Quantifying wider economic impacts of agglomeration for transport appraisal: existing evidence and future directions, Economics of Transportation, Vol:19, ISSN:2212-0122
et al., 2018, Evaluating the causal economic impacts of transport investments: evidence from the Madrid-Barcelona high speed rail corridor, Journal of Applied Statistics, Vol:46, ISSN:0266-4763, Pages:1714-1723
Horcher D, Graham DJ, 2018, Demand imbalances and multi-period public transport supply, Transportation Research Part B - Methodological, Vol:108, ISSN:0191-2615, Pages:106-126
Horcher D, Graham DJ, Anderson RJ, 2017, Crowding cost estimation with large scale smart card and vehicle location data, Transportation Research Part B - Methodological, Vol:95, ISSN:0191-2615, Pages:105-125
Graham DJ, Li H, 2016, Quantifying the causal effects of 20 mph zones on road casualties in London via doubly robust estimation, Accident Analysis & Prevention, Vol:93, ISSN:0001-4575, Pages:65-74
Graham DJ, McCoy EJ, Stephens DA, 2016, Approximate Bayesian inference for doubly robust estimation, Bayesian Analysis, Vol:11, ISSN:1931-6690, Pages:47-69
McCoy EJ, Graham DJ, Stephens DA, 2014, Quantifying causal effects of road network capacityexpansions on traffic volume and density via a mixedmodel propensity score estimator, Journal of the American Statistical Association, Vol:109, ISSN:1537-274X
Li H, Graham DJ, Majumdar A, 2014, Effects of changes in road network characteristics on road casualties: An application of full Bayes models using panel data, Safety Science, Vol:72, ISSN:0925-7535, Pages:283-292
Ramli AR, Graham DJ, 2013, The demand for road transport diesel fuel in the UK: Empirical evidence from static and dynamic cointegration techniques, Transportation Research Part D - Transport and Environment, Vol:26, ISSN:1361-9209, Pages:60-66
Li H, Graham DJ, Majumdar A, 2013, The impacts of speed cameras on road accidents: An application of propensity score matching methods, Accident Analysis & Prevention, Vol:60, ISSN:0001-4575, Pages:148-157
Li H, Graham DJ, Majumdar A, 2012, The effects of congestion charging on road traffic casualties: A causal analysis using difference-in-difference estimation, Accident Analysis & Prevention, Vol:49, ISSN:0001-4575, Pages:366-377
Graham DJ, McCoy EJ, Stephens DA, 2012, Quantifying the effect of area deprivation on child pedestrian casualties by using longitudinal mixed models to adjust for confounding, interference and spatial independence, Journal of the Royal Statistical Society Series A - Statistics in Society, Vol:176, ISSN:0964-1998