The conventional network structure measure, betweenness centrality (BC), has been used to predict roadway traffic flow; however, without consideration of variable travel demand on the roadway network, the measure’s prediction capability is limited by its static nature. With the objective of explicitly addressing the effects of travel pattern [e.g., origin–destination (O-D) distribution] on the prediction of urban roadway traffic flow, this paper proposes a modified measure that integrates the BC index and the traditional travel demand metrics (i.e., the O-D demand and the ratios of total demand). In the case study, roadway networks of two cities—San Francisco, California, and Nanjing, China—were selected to demonstrate the effectiveness of the proposed method. Taxi GPS trace data in the two cities were retrieved and applied to calibrate and validate the proposed measure. Correlation analyses were conducted to compare the predictions of the conventional and modified betweenness measures against the observed taxi traffic flows. The results show that, compared with the traditional approach, the modified betweenness measure produces predictions that have better correlations with the observed taxi traffic flows in both case studies. More accurate predictions for the future year traffic flow can be expected with the application of the new modified measure.

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Individual travel prediction is very important for the construction of intelligent urban transportation systems. Previous studies mainly focus on the improvement of algorithms, but pay little attention to the mining of data information. In this paper, the concept of the travel pattern is introduced into the field of individual travel prediction of frequent bus passengers. The travel pattern of passengers refers to the trip with similar boarding time and similar boarding and alighting stations of the same person. Through clustering the travel pattern by DBSCAN algorithm, the regularity of passenger travel can be better exploited and travel information can be integrated into a unified unit as well. In the process of prediction, we first predict whether the passenger will travel, and then, if so, predict the probability distribution of the next trip conditional on the previous one. The proposed method is tested using the Automatic Fare Collection data of Chengdu’s frequent bus passengers in May 2019. Based on travel pattern, the average accuracy of travel information prediction is about 41%, which is 13% higher than the method without using travel pattern. Furthermore, this paper also discusses the influence of spatial threshold in clustering on the prediction results.

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Bus queuing occurs frequently at the entry and exit areas of curbside stops. Formed queues can induce extra delays for bus operations. Conventional regression-based models to analyze bus service time cannot capture such delays because of their limitations in addressing interactions between buses and arriving passengers. To capture the extra delays, this paper proposes a new approach to estimate bus service time on the basis of the Monte Carlo method. The proposed models account for interactions between arriving buses as well as the numbers of boarding and alighting passengers. The models were established for curbside stops with both one and two berths. Case studies were implemented to show the effectiveness of the proposed approach. Archived data from the automatic vehicle location system and the automatic fare collection system were used to calibrate and validate the models. With the established models, the impact of passenger arrivals on bus service time was further demonstrated.