Journal of Transportation Engineering, Part A: Systems

Increasing awareness of the problems posed by anthropogenic climate change in recent decades has led to growing concerns over the level of carbon emissions attributable to travel, transport, and infrastructure development. Although rail is a relatively low-carbon mode, there is also pressure to reduce rail’s carbon emissions to help mitigate the extent of climate change. This paper provides a lifecycle analysis study which estimates the effect on carbon emissions of installing under sleeper pads (USPs) during track renewal on two conventional railway lines in the United Kingdom. The aims and scope of the analysis, the lifecycle inventory method, units used, and lifespan of the intervention can all have an important influence on the result. Although the installation of USPs at track renewal could reduce carbon (${\mathrm{CO}}_2$) emissions, these are very low compared with the associated financial and economic impacts, and some considerable time would be needed to generate these advantages. However, USPs might make rail travel cheaper, and the increased demand could be an important indirect effect on ${\mathrm{CO}}_2$ emissions in the transport system due to the shift from other transport modes. The use of nonrecycled rubber can offset the potential environmental benefits from the reduced requirement for track system interventions (maintenance and renewal needs), so the use of recycled carbon-neutral rubber in USP manufacturing should be incentivized.

With wireless communication and autonomous vehicle control capabilities, automated vehicle technology has the potential to improve the performance of an intersection. The objective of this research was to develop an intersection control algorithm that can jointly optimize the system performance and the trajectory of every single vehicle. An optimization algorithm was developed for a four-approach intersection with the consideration of turning movements and a full set of possible phases under a 100% automated vehicle environment. The intersection controller makes decisions on the vehicle passing sequence using a genetic algorithm–based optimization method, and at the same time it calculates the optimal vehicle trajectories. The optimization process repeats over a time horizon to process continually arriving vehicles. The performance of the proposed algorithm was assessed in various scenario-based simulation experiments and the results were compared with the actuated signal control. It was concluded that the proposed algorithm is able to reduce the intersection average travel time delay by 16.3% to 79.3%, depending on the demand scenario.

In this paper, we develop a novel approach to identify bottlenecks on highways using probe data collected by commercial global positioning system (GPS) fleet management devices installed in trucks. Further, the bottlenecks are classified based on the type of infrastructure present. Three main tasks were undertaken: (1) identification and classification of infrastructure at highway bottleneck locations, (2) examination and comparison between the various types of bottlenecks, and finally (3) prediction and classification of bottlenecks on highways using a decision tree classifier. Spatial and temporal variations of speed profile were primarily used for the identification and classification of bottlenecks. The results show that different types of bottlenecks due to construction zones, the presence of intersections, and toll plazas can be identified with high accuracy. Additionally, the presence of flyovers and bridges can also be detected from this speed profile. The findings of this study show that GPS data can not only be used to predict the locations of bottlenecks but also provide insightful information about the type of infrastructure, which is useful for highway operations and management.