Journal of Transportation Engineering, Part A: Systems

This paper develops a signal-optimization model for dynamic intersection control and dilemma-zone protection, where the decisions of signal phase switching are optimized to minimize the system transition cost weighted by the number of vehicles trapped in the dilemma zone and the delay incurred by queued vehicles at the intersection. The proposed model is solved with a model predictive control framework, and traffic dynamics are formulated with a cell transmission model, which offers the advantage of taking real-time traffic dynamics as the control input. Case study results from comparing the proposed model with actuated and fixed-time signal control strategies demonstrate the validity and advantages of the proposed methodology.

Maximum gradient (MG) decision-making is among the most important in railway alignment design because it greatly affects railway transport capacity, construction costs, and operation costs. However, existing studies mainly focus on optimizing railway alignment for cases with predetermined MG values. Studies on MG decision-making are rare. In this study, a data-driven method is proposed for MG decision-making based on a convolutional neural network (CNN). A total of 246 existing established railway cases are compiled whose total length is nearly 30,000 km. Factors that influence MG decision-making are characterized as a multichannel image. The 246 railway cases are characterized as 246 multichannel images and cropped into 20,170 images. Using the cropped images as the input data, a CNN model is designed to explore the relations among the factors and the MG value in order to make MG decisions. The method’s performance is tested on 36 existing railway cases. The test accuracy is 94.44%, which demonstrates that the proposed method can match experienced human experts in determining MG values for railway cases.

The adoption of battery electric vehicles (BEVs) is widely expected to help improve air quality in Chinese cities, whereas this adoption may change traffic status related to vehicle energy consumption and emissions. This paper presents a traffic status-sensitive approach to evaluating the environmental effects and accounting for the potential impacts of BEVs on traffic flows. First, a multinomial logit (MNL) BEV route choice model is estimated, and the different route choice preferences between BEV and gasoline vehicle (GV) drivers are analyzed. Second, a stochastic assignment approach for mixed electric and gasoline vehicular flows is developed, incorporating BEV and gasoline vehicle (GV) route choice models. Finally, using actual vehicle trip data, the proposed approach is applied to calculate the traffic status and evaluate the environmental effects under various BEV penetrations in Beijing. The results indicate that the relative decreases in emissions are less than the BEV penetration rate, and increasing BEVs appears to change network traffic status and then increase the emission rates of GVs, especially during peak hours.

Tamping affects track geometry irregularity indexes to different extents. The recovery measure of tamping on different track geometry irregularity indexes plays an important guiding role in the scientific decision making related to tamping operations and for the effective management and control of track quality. In this study, the recovery rates of track geometry quality and track geometry degradation rates between two adjacent tamping operations corresponding to each track geometry irregularity index were used as recovery measure indexes to establish models for the recovery measure of tamping on different track geometry irregularity indexes. Box plots were used for comparative analyses of the recovery effects of tamping on different track geometry irregularity indexes. To verify the effectiveness of these models, a total of 31 track inspection car historical data from September 2014 to December 2016 and tamping operation maintenance data during this time from the Shenmu–Shuozhou railway Hexi transportation division was used. The results showed that the recovery measure models proposed in this study can be used to effectively guide tamping operations. The tamping strategy based on these models helped significantly improve the tamping of the Shenmu–Shuozhou railway in terms of the tamping length of track and tamping cost. This was verified by comparing with the traditional tamping strategy and the tamping strategy based on the longitudinal level.