Vehicle re-identification aims to match and identify the same vehicle crossing multiple surveillance cameras and obtain traffic information such as travel time. The Automatic License Plate Recognition (ALPR) data are widely employed in urban surveillance. However, vehicle re-identification based on ALPR data is challenging due to license plate recognition errors and unrecognized issues. This paper proposes a vehicle matching algorithm designed to maximize the travel time probability using ALPR data, while accounting for recognition errors and unrecognized issues. The proposed algorithm consists of several modules, including the estimation of travel time distribution, computation of travel time probability, calculation of travel time confidence intervals and matching time window size, restricted fuzzy matching, and vehicle matching optimization. To evaluate the effectiveness of the proposed algorithm across varying lighting and weather conditions, ALPR data was collected from a survey road in four scenarios: sunny day, sunny night, rainy day, and rainy night. The results indicate that when compared to a sunny day scenario, severe lighting and adverse weather conditions lead to decreased matching accuracy and increased matching accuracy errors for all methods evaluated. However, our proposed model outperforms benchmark algorithms in both scenarios, demonstrating its superior performance.

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.

The collection of individual GPS data, as a substitute for traditional travel surveys, is hindered by low response rates and high costs. Meanwhile, passive mobile phone data, such as Location-Based Service (LBS) data, has high user penetration but remains unexplored for travel behavior analysis. The irregular frequency of data collection results in distorted features and non-uniformly distributed information of trajectories in the Travel Mode Identification (TMI) task. The diversity of trajectories also poses challenges for TMI models. To fill the gap, we propose a TMI framework named Trajectory-as-a-Sequence for Non-uniform data (TaaSN). Specifically, we incorporate GIS features to address the sparsity of motion features. Then, we design a model structure that accounts for time gaps, capturing non-uniform information of trajectory due to irregular frequency. To further enhance model’s generalizability to diverse trajectory data, we propose a trajectory point dropout training strategy. The experimental results demonstrate that the TaaSN framework can greatly exploit the potential of LBS data in travel behavior mining. The proposed model achieves high accuracy on both non-uniform and uniform trajectories. On pseudo-LBS data, the accuracy reaches 84.9% when applied to the trajectories of existing travelers, and achieves 83.5% when applied to the trajectories of new travelers. On uniform data, it reaches 86.8% and 85.4%, respectively. Furthermore, we carry out comprehensive experiments and conclude valuable insights for future TMI framework design.

Discretionary lane changing is one of the most important behaviors in vehicle dynamics. The analysis of discretionary lane changing can provide support for human-like autonomous driving system and microscopic traffic simulation. The rule based discretionary lane change decision-making model has the advantages of good interpretability and being in line with human intuitiveness, but the performance of the rule based models are relatively poor. The learning-based discretionary lane changing decision-making models perform better in lane changing analysis than that of the rule based models, while they require a large amount of training data and have poor interpretability. This study proposes a framework for discretionary lane changing decision-making based on cumulative prospect theory and decision tree model, and uses genetic algorithm to calibrate the parameters of the framework. The framework proposed in this study has good interpretability for discretionary lane changing behavior similar to rule-based models, and it can achieve good lane changing prediction performance. The Next Generation Simulation (NGSIM) dataset is used to validate the framework proposed in this study. The results show that the framework proposed in this study can achieve better performance of discretionary lane change prediction than the analysis model based on decision tree and the analysis model based on cumulative prospect theory. When predicting discretionary lane changing behavior 2–0 seconds in advance, the accuracy of the framework in the test set can reach 85.6%.