The influence of adverse weather conditions on probability of congestion on Dutch motorways

  • Wouter J.H. van Stralen Delft University of Technology
  • Simeon C. Calvert TNO
  • Eric J.E. Molin Delft University of Technology


Weather conditions are widely acknowledged to contribute to the occurrence of congestion on motorway traffic by influencing both traffic supply and traffic demand. To the best of our knowledge, this is the first paper that explicitly integrates supply and demand effects in predicting the influence of adverse weather conditions on the probability of occurrence of congestion. Traffic demand is examined by conducting a stated adaptation experiment, in which changes in travel choices are observed under adverse weather scenarios. Based on these choices, a Panel Mixed Logit model is estimated. Supply effects are taken into account by examining the influence of precipitation on motorway capacity. Based on the Product Limit Method, capacity distribution functions are estimated for dry weather, light rain and heavy rain. With the developed model to integrate the supply and demand effects breakdown probabilities can be calculated for any given traffic demand and capacity. The results show that rainfall leads to a significant increase in the probability of traffic breakdown at bottleneck locations. Interestingly the probability of a breakdown at these bottleneck locations is predicted to be slightly higher in light rain (98.7%) than in heavy rain (95.7%) conditions, which is the result of the higher traffic demand in light rain conditions. Based on the results presented in this paper, it can be recommended to always incorporate both supply and demand effects in the predictions of motorway breakdown probabilities due to adverse weather conditions to improve the validity of the predictions.

How to Cite
VAN STRALEN, Wouter J.H.; CALVERT, Simeon C.; MOLIN, Eric J.E.. The influence of adverse weather conditions on probability of congestion on Dutch motorways. European Journal of Transport and Infrastructure Research, [S.l.], v. 15, n. 4, sep. 2015. ISSN 1567-7141. Available at: <>. Date accessed: 16 feb. 2019. doi: