Journals

  • Garrido-Valenzuela, F., Cats, O., van Cranenburgh, S. (2023). Where are the people? Counting people in millions of street-level images to explore associations between people's urban density and urban characteristics. Computers, Environment and Urban Systems. [https://doi.org/10.1016/j.compenvurbsys.2023.101971
  • Cats, O. (2023). Identifying human mobility patterns using smart card data. Transport Reviews. [https://doi.org/10.1080/01441647.2023.2251688
  • Calvert, S., van Arem, B., Lappin, J. (2023). Herd immunity for traffic safety in mixed automated traffic: what if cars could not crash!?. Transportation Letters. [https://doi.org/10.1080/19427867.2022.2074697
  • Jiao, Y., Calvert, S., van Cranenburgh, S., van Lint, H. (2023). Inferring vehicle spacing in urban traffic from trajectory data. Transportation Research Part C: Emerging Technologies. [https://doi.org/10.1016/j.trc.2023.104289
  • Spierenburg, L., van Cranenburgh, S., Cats, O. (2023). Characterizing residential segregation in cities using intensity, separation, and scale indicators. Computers, Environment and Urban Systems. [https://doi.org/10.1016/j.compenvurbsys.2023.101990
  • Smeele, N., Chorus, C., Schermer, M., de Bekker-Grob, E. (2023). Towards machine learning for moral choice analysis in health economics: A literature review and research agenda. Social Science and Medicine. [https://doi.org/10.1016/j.socscimed.2023.115910
  • Garrido-Valenzuela, F., Cruz, D., Dragicevic, M., Schmidt, A., Moya, J., Tamblay, S., Herrera, J., Muñoz, J. (2022). Identifying and visualizing operational bottlenecks and Quick win opportunities for improving bus performance in public transport systems. Transportation Research Part A: Policy and Practice. [https://doi.org/10.1016/j.tra.2022.08.005
  • Cats, O., Ferranti, F. (2022). Voting with one's feet: Unraveling urban centers attraction using visiting frequency. Cities. [https://doi.org/10.1016/j.cities.2022.103773
  • Kroesen, M., van Wee, B. (2022). Understanding how accessibility influences health via active travel: Results from a structural equation model. Journal of Transport Geography. [https://doi.org/10.1016/j.jtrangeo.2022.103379
  • Cats, O., Ferranti, F. (2022). Unravelling individual mobility temporal patterns using longitudinal smart card data. Research in Transportation Business and Management. [https://doi.org/10.1016/j.rtbm.2022.100816
  • Garrido-Valenzuela, F., Raveau, S., Herrera, J. (2022). Bayesian Route Choice Inference to Address Missed Bluetooth Detections. IEEE Transactions on Intelligent Transportation Systems. [https://doi.org/10.1109/TITS.2020.3028804
  • van Cranenburgh, S., Wang, S., Vij, A., Pereira, F., Walker, J. (2022). Choice modelling in the age of machine learning - Discussion paper. Journal of Choice Modelling. [https://doi.org/10.1016/j.jocm.2021.100340
  • Calvert, S., van Arem, B., Heikoop, D., Hagenzieker, M., Mecacci, G., de Sio, F. (2021). Gaps in the Control of Automated Vehicles on Roads. IEEE Intelligent Transportation Systems Magazine. [https://doi.org/10.1109/MITS.2019.2926278
  • van Cranenburgh, S., Kouwenhoven, M. (2021). An artificial neural network based method to uncover the value-of-travel-time distribution. Transportation. [https://doi.org/10.1007/s11116-020-10139-3
  • Cats, O., Hijner, A. (2021). Quantifying the cascading effects of passenger delays. Reliability Engineering and System Safety. [https://doi.org/10.1016/j.ress.2021.107629
  • Calvert, S., Mecacci, G., Heikoop, D., Janssen, R. (2021). How to ensure control of cooperative vehicle and truck platoons using meaningful human control. European Journal of Transport and Infrastructure Research. [https://doi.org/10.18757/ejtir.2021.21.2.5354
  • Jiao, Y., Li, Y. (2021). An active opinion dynamics model: the gap between the voting result and group opinion. Information Fusion. [https://doi.org/10.1016/j.inffus.2020.08.009
  • Calvert, S., Arem, B. (2020). Cooperative adaptive cruise control and intelligent traffic signal interaction: A field operational test with platooning on a suburban arterial in real traffic. IET Intelligent Transport Systems. [https://doi.org/10.1049/iet-its.2019.0742
  • Calvert, S., Schakel, W., van Lint, J. (2020). A generic multi-scale framework for microscopic traffic simulation part II – Anticipation Reliance as compensation mechanism for potential task overload. Transportation Research Part B: Methodological. [https://doi.org/10.1016/j.trb.2020.07.011
  • Calvert, S., Klunder, G., Steendijk, J., Snelder, M. (2020). The impact and potential of cooperative and automated driving for intelligent traffic signal corridors: A field-operational-test and simulation experiment. Case Studies on Transport Policy. [https://doi.org/10.1016/j.cstp.2020.05.011
  • Calvert, S., Heikoop, D., Mecacci, G., van Arem, B. (2020). A human centric framework for the analysis of automated driving systems based on meaningful human control. Theoretical Issues in Ergonomics Science. [https://doi.org/10.1080/1463922X.2019.1697390
  • Calvert, S., van Arem, B., van Lint, J. (2020). Corrigendum to “A generic multi-level framework for microscopic traffic simulation with automated vehicles in mixed traffic” [Transport. Res. Part C: Emerg. Technol. 110 (2020) 291–311] (Transportation Research Part C (2020) 110 (291–311), (S0968090X19304322), (10.1016/j.trc.2019.11.019)). Transportation Research Part C: Emerging Technologies. [https://doi.org/10.1016/j.trc.2020.102643
  • Calvert, S., Mecacci, G. (2020). A Conceptual Control System Description of Cooperative and Automated Driving in Mixed Urban Traffic with Meaningful Human Control for Design and Evaluation. IEEE Open Journal of Intelligent Transportation Systems. [https://doi.org/10.1109/OJITS.2020.3021461
  • Calvert, S., van Arem, B. (2020). A generic multi-level framework for microscopic traffic simulation with automated vehicles in mixed traffic. Transportation Research Part C: Emerging Technologies. [https://doi.org/10.1016/j.trc.2019.11.019