Narrow and closed spaces like high-speed train cabins are at great risk for airborne infectious disease transmission. With the threat of COVID-19 as well as other potential contagious diseases, it is necessary to protect passengers from infection. Except for the traditional preventions such as increasing ventilation or wearing masks, this paper proposes a novel measurement that optimizes passenger-to-car assignment schemes to reduce the infection risk for high-speed railway passengers. First, we estimated the probability of an infected person boarding the train at any station. Once infectors occur, the non-steady-state Wells–Riley equation is used to model the airborne transmission intercar cabin. The expected number of susceptible passengers infected on the train can be calculated, which is the so-called overall infection risk. The model to minimize overall infection risk, as a pure integer quadratic programming problem, is solved by LINGO software and tested on several scenarios compared with the classical sequential and discrete assignment strategies used in China. The results show that the proposed model can reduce 67.6% and 56.8% of the infection risk in the base case compared to the sequential and discrete assignment, respectively. In other scenarios, the reduction lies mostly between 10% and 90%. The optimized assignment scheme suggests that the cotravel itinerary among passengers from high-risk and low-risk areas should be reduced, as well as passengers with long- and short-distance trips. Sensitivity analysis shows that our model works better when the incidence is higher at downstream or low-flow stations. Increasing the number of cars and car service capacity can also improve the optimization effect. Moreover, the model is applicable to other epidemics since it is insensitive to the Wells–Riley equation parameters. The results can provide a guideline for railway operators during the post-COVID-19 and other epidemic periods.

Identifying travel modes from Global Navigation Satellite System (GNSS) trajectories is helpful for traffic management. In mode identification, the motion features are extracted from trajectories to train the classifiers. However, features would be distorted by the positioning noise when migrating existing frameworks to poor‐quality tracks. This study aims to answer how to eliminate the impact of positioning error on mode identification. Specifically, six widely used Trajectory Noise Reduction (TNR) methods were tested. Representative motion features were calculated and sent to several classical classifiers to evaluate the effect of TNR. Then, the extent to which TNR restores motion features is analysed by information gain. To verify the robustness of these methods, multiple noise scenarios are designed to simulate possible positioning noise. The results show that the trajectory smoothing methods perform better than the outlier elimination methods regardless of the type and magnitude of noise. In particular, the Gaussian kernel smoothing can achieve the highest effect in almost all noise scenarios. For untested TNR methods that require a time window radius parameter, a 30‐s time window is a good candidate. Moreover, the visualisation verification cannot ensure the best TNR method for travel mode identification.