Academic Research

Imperial Fringe

Introduction

WHO WE ARE

Our research group within the Transport Strategy Centre is led by Professor Dan Graham, co-Director of the TSC.

Our team of four Postdoctoral Research Associates and ten Research Postgraduates (PhD candidates) has a diverse profile in various fields of transport research, including quantitative as well as theoretical areas at the crossroad between applied economics, statistical modelling, econometrics, civil engineering and data science.

The group’s main objective is to contribute to the scientific dialogue surrounding planning and policy problems in transport through publications in top-tier peer-reviewed journals and conference presentations.

Our Role Within TSC

Our research group works in close collaboration with the applied research branch of the TSC and its international benchmarking groups. Research group members are involved in case studies and statistical analyses prepared as part of benchmarking cycles on a regular basis.

The research group contributes to the continuous revision and improvement of the statistical methodology behind the system of key performance indicators. Our group members are regular attendees of various meetings of the benchmarking groups facilitated by the TSC.

Join our research group

Recent Publications

  1. Acharya, P. S., AitBihiOuali, L., Matthews, H. S., & Graham, D. J. (2023). The Impact of Periodic Passenger Vehicle Safety Inspection Programs on Roadway Fatalities: Evidence from US States Using Panel Data. Journal of Transportation Engineering, Part A: Systems, 149(7), 04023052.
  2. Anupriya, Bansal, P., & Graham, D. J. (2023). Congestion in cities: Can road capacity expansions provide a solution?. Transportation Research Part A: Policy and Practice, 174, 103726.
  3. Anupriya, Graham, D. J., Bansal, P., Hörcher, D., & Anderson, R. (2023). Optimal congestion control strategies for near-capacity urban metros: Informing intervention via fundamental diagrams. Physica A: Statistical Mechanics and its Applications, 609, 128390.
  4. Awad, F. A., Graham, D. J., AitBihiOuali, L., & Singh, R. (2023). Performance of urban rail transit: a review of measures and interdependencies. Transport Reviews, 43(4), 698-725.
  5. Awad, F. A., Graham, D. J., AitBihiOuali, L., Singh, R., & Barron, A. (2023). Benchmarking the performance of urban rail transit systems: a machine learning application. Transportmetrica A: Transport Science, 2241566.
  6. Awad, F. A., Graham, D. J., Singh, R., & AitBihiOuali, L. (2023). Predicting urban rail transit safety via artificial neural networks. Safety science, 167, 106282.
  7. Gong, Z., Zhang, F., Liu, W., & Graham, D. J. (2023). On the effects of airport capacity expansion under responsive airlines and elastic passenger demand. Transportation Research Part B: Methodological, 170, 48-76.
  8. Hörcher, D., De Borger, B., & Graham, D. J. (2023). Subsidised transport services in a fiscal federation: Why local governments may be against decentralised service provision. Economics of Transportation, 34, 100312.
  9. Luo, Y., Graham, D. J., & McCoy, E. J. (2023). Semiparametric Bayesian doubly robust causal estimation. Journal of Statistical Planning and Inference, 225, 171-187.
  10. Ma, L., Graham, D. J., & Stettler, M. E. (2023). Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London. Environmental Science & Technology.
  11. Singh, R., Hörcher, D., & Graham, D. J. (2023). An evaluation framework for operational interventions on urban mass public transport during a pandemic. Scientific Reports, 13(1), 5163.
  12. Xu, M., Anupriya, & Bansal, P. (2023). Surge pricing and consumer surplus in the ride-hailing market: Evidence from China. Travel Behaviour and Society, 33, 100638.
  13. Xuto, P., Anderson, R. J., Graham, D. J., & Hörcher, D. (2023). Sustainable urban rail funding: Insights from a century-long global dataset. Transport Policy, 130, 100-115.
  14. Xuto, P., Bansal, P., Anderson, R. J., Graham, D. J., Hörcher, D., & Barron, A. (2023). Examining the impacts of capital investment in London’s Underground: A long-term analysis. Transportation Research Part A: Policy and Practice, 175, 103744.
  15. Zimmo, I., Hörcher, D., Singh, R., & Graham, D. J. (2023). Benchmarking Travel Time and Demand Prediction Methods Using Large-scale Metro Smart Card Data. Periodica Polytechnica Transportation Engineering.

Research Areas

Our core fields of research interest include causal inference analysis of various topics spanning between urban economics through accident research to the statistical assessment of transport policies using large-scale public transport datasets. We are a leading research group in the measurement of urban agglomeration economies and applications in investment appraisal and cost-benefit analysis.

The group actively participates in the international community of transport economics, with both theoretical and empirical analyses, most importantly in topics related to pricing and capacity optimisation, cost modelling and benchmarking in public transport. In recent years we gained experience in the disaggregate statistical analysis of large-scale smart card and vehicle location datasets, focusing on the measurement of the user cost of crowding, passenger-to-train assignment, the decomposition of journey time components, and incident detection and prevention.

Our research

Rail Control Room

Performance analytics and data centric engineering

Modern signalling and electronic fare payment systems of urban public transport networks collect millions of data records on a daily basis. Our aim is to bring state-of-the-art data science, statistical modelling and data mining closer to public transport operators. This research area covers the theory of transport benchmarking, cost and aggregate demand modelling, travel time and dwell time analyses, passenger-to-train assignment, and a variety subject areas with technical transport content.
Liverpool Street Station

Causal inference methods in transport research

Identifying causal effects is central for us as we try to isolate the sole impact of the transport intervention net of other effects. Researchers at the TSC make the best use of state-of-the-art econometric techniques to identify causality in transport dynamics.

Our research

KL

Transport and spatial economics

With applied microeconomic theory we explain the core mechanisms behind travel demand and the efficiency of transport policies. Our group has over two decades of experience in the empirical and theoretical analysis of the wider economic benefits of transport improvements, with a specific focus on the measurement of agglomeration externalities. The group contributes to the transport economics community with new studies on the trade-offs behind pricing, capacity provision and subsidies in public transport.
Kings Cross

Resilience, risk and safety analyses

Accident prevention and the improvement of the resilience of urban transport systems are one of the key priorities of contemporary transport policy. We use advance statistical methods to detect and explain incidents using large-scale demand and supply side datasets. Our empirical analyses put passenger experience to the centre of the mitigation of accident and incident impacts.

Academic Research Group Members

Research Group Members

Daniel J. Graham

Daniel J. Graham
Professor of Statistical Modelling

Anupriya

Anurpiya

Anupriya
Turing Postdoctoral Research Associate (Casual Staff)

Daniel Hörcher

Daniel Hörcher
Postdoctoral Research Associate

Liang Ma

Liang Ma

Liang Ma
Postdoctoral Research Associate

Nan Zhang

Nan Zhang

Nan Zhang
Postdoctoral Research Associate

PhD Candidates

Kailin Chen

Kailin Chen

Kailin Chen
PhD Candidate

Tobie Cusson

Tobie Cusson

Tobie Cusson
PhD Candidate

Supraja, Hariharan

Supraja, Hariharan
PhD Candidate

Jia Ming Li

Jia Ming Li
PhD Candidate

Surabhi Ojha

Surabhi Ojha
PhD Candidate

Alumni

Farah Awad

Farah Awad

Farah Awad
PhD Alumni

Ramandeep Singh

Ramandeep Singh

Ramandeep Singh
PhD and Postdoc Alumni

Haojie Li

Haoji Li

Haojie Li
PhD Alumni

Csaba Pogonyi

Csaba Pogonyi

Csaba Pogonyi
PhD Alumni

Shane Canavan

Shane Canavan

Shane Canavan
PhD Alumni

Zia Wadud

Zia Wadud

Zia Wadud
PhD Alumni

Praj Xuto

Praj Xuto

Praj Xuto
PhD Alumni

Our Publications in 2022

  1. Anupriya, Graham, D. J., Bansal, P., Hörcher, D., & Anderson, R. (2022). Optimal congestion control strategies for near-capacity urban metros: Informing intervention via fundamental diagrams. Physica A: Statistical Mechanics and its Applications, 128390.
  2. Anupriya, Bansal, P., & Graham, D. J. (2022). Modelling the propagation of infectious disease via transportation networks. Scientific Reports, 12(1), 1-12.
  3. Zhang, N., Graham, D. J., Bansal, P., & Hörcher, D. (2022). Detecting metro service disruptions via large-scale vehicle location data. Transportation Research Part C: Emerging Technologies, 144, 103880.
  4. Xuto, P., Anderson, R. J., Graham, D. J., & Hörcher, D. (2022). Sustainable urban rail funding: Insights from a century-long global dataset. Transport Policy.
  5. Graham, D. J., & Singh, R. (2022). Model-based adjustment for conditional benchmarking. IMA Journal of Management Mathematics, 33(3), 381-393.
  6. Bansal, P., Kessels, R., Krueger, R., & Graham, D. J. (2022). Preferences for using the London Underground during the COVID-19 pandemic. Transportation Research Part A: Policy and Practice, 160, 45-60.
  7. Hörcher, D., & Graham, D. J. (2022). Multimodal substitutes in public transport: Efficient variety or wasteful competition?. Journal of Transport Economics and Policy (JTEP), 56(1), 1-27.
  8. Singh, R., Sood, R., & Graham, D. J. (2022). Road traffic casualties in Great Britain at daylight savings time transitions: a causal regression discontinuity design analysis. BMJ open, 12(4), e054678.
  9. Bansal, P., Hörcher, D., & Graham, D. J. (2022). A dynamic choice model to estimate the user cost of crowding with large scale transit data. J. Roy. Stat. Soc.: Ser. A, 2022, 1-25.
  10. Hörcher, D., Singh, R., & Graham, D. J. (2022). Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis. Transportation, 49(2), 735-764.
  11. Graham, D. J., Hörcher, D., & Vickerman, R. (2022). Infrastructure and the competitiveness of cities. In: Gómez-Ibáñez, J. & Liu, Z. (Eds) Infrastructure Economics and Policy: International Perspectives. Lincoln Institute of Land Policy, Cambridge, Mass., USA, pp. 120-139.

Our Publications in 2021

  1. Singh, R., Graham, D. J., Hörcher, D., & Anderson, R. J. (2021). The boundary between random and non-random passenger arrivals: Robust empirical evidence and economic implications. Transportation Research Part C: Emerging Technologies, 130, 103267.
  2. Ait Bihi Ouali, L., Musuuga, D., & Graham, D. J. (2021). Quantifying responses to changes in the jurisdiction of a congestion charge: A study of the London western extension. PloS one, 16(7), e0253881.
  3. Graham, D.J. (2021) Causal inference for ex post evaluation of transport interventions. In: Vickerman, Roger (eds.) International Encyclopedia of Transportation. vol. 1. UK: Elsevier Ltd.
  4. Hörcher, D. (2021) Value of crowding. In: Vickerman, Roger (eds.) International Encyclopedia of Transportation. vol. 1, pp. 84-88. UK: Elsevier Ltd.
  5. Monchambert, G., Hörcher, D., Coulombel, N., Tirachini, A. (2021) Subway systems. In: Vickerman, Roger (eds.) International Encyclopedia of Transportation. vol. 1. UK: Elsevier Ltd.
  6. Li, H., Zhu, M., Graham, D. J., & Ren, G. (2021). Evaluating the speed camera sites selection criteria in the UK. Journal of Safety Research, 76, 90-100.
  7. Kazemzadeh, K., & Bansal, P. (2021). Electric bike navigation comfort in pedestrian crowds. Sustainable Cities and Society, 69, 102841.
  8. Hörcher, D., Singh, R., & Graham, D. J. (2021). Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis. Transportation, 1-30.
  9. Hörcher, D., & Tirachini, A. (2021). A review of public transport economics. Economics of Transportation, 25, 100196.
  10. Xuto, P., Anderson, R. J., Graham, D. J., & Hörcher, D. (2021). Optimal infrastructure reinvestment in urban rail systems: A dynamic supply optimisation approach. Transportation Research Part A: Policy and Practice, 147, 251-268.
  11. Ait Bihi Ouali, L., & Graham, D. J. (2021). The impact of the MeToo scandal on women’s perceptions of security. Transportation Research Part A: Policy and Practice, 147, 269-283.
  12. Bansal, P., Dua, R., Krueger, R., & Graham, D. J. (2021). Fuel Economy Valuation and Preferences of Indian Two-wheeler Buyers. Journal of Cleaner Production, 126328.
  13. Bansal, P., Krueger, R., & Graham, D. J. (2021). Fast Bayesian estimation of spatial count data models. Computational Statistics & Data Analysis, 157, 107152.
  14. Zhang, N., Graham, D. J., Hörcher, D., & Bansal, P. (2021). A causal inference approach to measure the vulnerability of urban metro systems. Transportation
  15. Ma, L., Graham, D. J., & Stettler, M. E. (2021). Air quality impacts of new public transport provision: A causal analysis of the Jubilee Line Extension in London. Atmospheric Environment, 245, 118025.
 

Our Publications in 2020

  1. Li, H., Zhu, M., Graham, D. J., & Ren, G. (2020). Evaluating the speed camera sites selection criteria in the UK. Journal of Safety Research.
  2. Carbo, J. M., & Graham, D. J. (2020). Quantifying the impacts of air transportation on economic productivity: a quasi-experimental causal analysis. Economics of Transportation, 24, 100195.
  3. Anupriya, Graham, D. J., Hörcher, D., Anderson, R. J., & Bansal, P. (2020). Quantifying the ex-post causal impact of differential pricing on commuter trip scheduling in Hong Kong. Transportation Research Part A: Policy and Practice, 141, 16-34.
  4. Li, H., Wu, D., Graham, D. J., & Sze, N. N. (2020). Comparison of exposure in pedestrian crash analyses: A study based on zonal origin-destination survey data. Safety Science, 131, 104926.
  5. Singh, R., Graham, D. J., & Anderson, R. J. (2020). Quantifying the effects of passenger-level heterogeneity on transit journey times. Data-Centric Engineering, 1.
  6. Hörcher, D., & Graham, D. J. (2020). The Gini index of demand imbalances in public transport. Transportation, 1-24.
  7. Anupriya, Graham, D. J., Carbo, J. M., Anderson, R. J., & Bansal, P. (2020). Understanding the costs of urban rail transport operations. Transportation Research Part B: Methodological, 138, 292-316.
  8. Ait Bihi Ouali, L., Carbo, J. M., & Graham, D. J. (2020). Do changes in air transportation affect productivity? A cross‐country panel approach. Regional Science Policy & Practice, 12(3), 493-505.
  9. Ait Bihi Ouali, L., Graham, D. J., Barron, A., & Trompet, M. (2020). Gender differences in the perception of safety in public transport. Journal of the Royal Statistical Society: Series A (Statistics in Society), 183(3), 737-769.
  10. Hörcher, D., & Graham, D. J. (2020). MaaS economics: Should we fight car ownership with subscriptions to alternative modes?. Economics of Transportation, 22, 100167.
  11. Li, H., Zhu, M., Graham, D. J., & Zhang, Y. (2020). Are multiple speed cameras more effective than a single one? Causal analysis of the safety impacts of multiple speed cameras. Accident Analysis & Prevention, 139, 105488.
  12. Singh, R., Hörcher, D., Graham, D. J., & Anderson, R. J. (2020). Decomposing journey times on urban metro systems via semiparametric mixed methods. Transportation Research Part C: Emerging Technologies, 114, 140-163.
  13. Zhang, F., & Graham, D. J. (2020). Air transport and economic growth: a review of the impact mechanism and causal relationships. Transport Reviews, 40(4), 506-528.
  14. Morse, L., Trompet, M., Barron, A., Anderson, R., & Graham, D. J. (2020). A benchmarking framework for understanding bus performance in the US. Benchmarking: An International Journal.
  15. Hörcher, D., De Borger, B., Seifu, W., & Graham, D. J. (2020). Public transport provision under agglomeration economies. Regional Science and Urban Economics, 81, 103503.