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Joint destination-mode travel choice models are developed for intercity long-distance travel among sixteen cities in Yangtze River Delta Megaregion of China. The model is developed for all the trips in the sample and also by two different trip purposes, work-related business and personal business trips, to accommodate different time values and attraction factors. A nested logit modeling framework is applied to model trip destination and mode choices in two different levels, where the lower level is a mode choice model and the upper level is a destination choice model. The utility values from various travel modes in the lower level are summarized into a composite utility, which is then specified into the destination choice model as an intercity impedance factor. The model is then applied to predict the change in passenger number from Shanghai to Yangzhou between scenarios with and without high-speed rail service to demonstrate the applicability. It is helpful for understanding and modeling megaregional travel destination and mode choice behaviors in the context of developing country.

Speed and punctuality are essential to the quality of bus services. To reduce bus delays and increase bus speed, a transit signal priority (TSP) method based on speed guidance and coordination among consecutive intersections is proposed. The TSP problem is formulated as a binary mixed integer nonlinear program. Impacts of TSP on the current intersection and adjacent intersection downstream are measured by deviations of split time from background timing plans and non-overlapping degrees, respectively. The weighted sum of the two measurements and bus travel time is taken as the objective function. The method does not change the original cycle length, and it is adaptive to timing plans with an arbitrary number of phases. Exclusive bus lanes are required to provide good conditions for speed guidance. A simulation case study of three consecutive intersections in Shanghai, China, is conducted. In the experiments, no priority method, the conventional TSP method, and the proposed method are applied. The results indicate that the proposed method performs the best. Compared to no priority method, the average travel time of buses, delays of bus, and delay per capita are reduced by 26.3%, 91.3%, and 14.5%, respectively. In addition, no serious deterioration is observed in the experience of other road users as the congestion level rises. The study illustrates the possibility of giving high priority to buses without significant negative impacts on other road users, and it can help traffic managers to alleviate traffic congestion in densely populated cities.

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The prediction of low visibility is essential for proactive traffic safety management on freeways under fog conditions. However, few studies have developed prediction models for visibility on freeways at a short‐term time interval. This study proposes an ensemble learning approach to develop a short‐term prediction model of low visibility on freeways using meteorological data. Spearman's rank correlation coefficient is used to select meteorological variables related to low visibility. Random forests (RF) and extreme gradient boosting (XGB) are employed to develop visibility prediction models, and back propagation neural network (BPNN) and logistic regression (LR) are used for comparison. The models are evaluated over five prediction time intervals (5, 10, 15, 30, and 60 min). The results indicate that the RF models outperform the other models with precision of 73.9%, recall of 59.8% and F1 score of 0.65. Moreover, the prediction model with a 15‐min time interval shows better performance. With the proposed short‐term prediction of low visibility, it is expected that more crashes could be prevented with more appropriate proactive traffic safety management strategies.

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