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The cost associated with the waiting time that passengers incur in a public transit network is one of the main components of total transit travel cost. The cost of a unit of waiting time per passenger is higher than the cost of a unit of riding time or access time. While the assumption of half the headway as the mean waiting time has been widely used in waiting time cost estimation, it is not always a realistic assumption considering heterogeneous passengers and different types of transit services. Moreover, many studies considered the waiting times of passengers only at the origin, while waiting times can also be incurred at transfer points and the destination, the latter especially for passengers with required arrival time. After describing definitions for type of passengers and type of transit service and reasoning about proper assumptions for mean waiting time, we conducted a comprehensive survey of articles in transit operation and planning published in highly-ranked journals from 2010 to 2019 which is presented in the paper. We found that most of the reviewed articles on transit suffer from lack of clear assumptions regarding the type of service and the type of passenger, which restricts the validity of the assumed waiting time. To address these issues, we develop a comprehensive approach to determine the mean waiting time of travellers. Mean waiting time for possible combinations of heterogeneous types of passengers (who plan and who do not plan their trips) and different service type (schedule-based, frequency-based, high-frequency and lowfrequency) are developed. In addition, we critically review the waiting time considered in previous studies for a single route case (uniform headway with reliable service). The proposed comprehensive approach could be utilised in transit studies to better model the transit use which subsequently results in better designs and more efficient operations.

A region's transportation sector is vital to its economic and social health. Transportation systems are also influenced by climate change directly and indirectly and on a variety of spatial and temporal scales. Under a changing climate, many regions around the globe and especially in urban areas, have experienced increases in flood intensity and frequency in recent decades. Flooding can strain transportation networks in both the short- and long-term through transportation delays, infrastructure damage, and recovery, and potentially affect economies. The present article is a review of how flooding impacts transportation networks in both short- and long-term timescales and their subsequent impact on resiliency of the network. Firstly, flood effects are classified based on the connections between the type of flooding and the type of impact (either direct or indirect) on the transportation system. An analysis of the assessment methods and the transport models used to formulate flood effects on the transportation system is provided, as well as the drawbacks from the context of timescales, and recommendations for future research. The analysis indicates that the majority of the articles assess the direct and tangible impacts with focus on the resilience of the transportation network in short- and medium-term temporal scales and at smaller spatial scales. There is less emphasis on indirect, intangible flood impacts, and long-term temporal scales.

Shanghai, China, has the largest metro system in the world, with a network length of more than 550 km. Both Shanghai and Beijing are among the top five cities in terms of ridership, and some of the most important components of their metro systems are the ring transit lines. Many other cities, in China and elsewhere, also envision a ring transit line for their future rail transit networks. A previously developed analytical model for the long-range planning of ring transit lines was used in the comparison of the current alignment of the Shanghai ring line with the optimized model output, and a second ring transit line was recommended for the future Shanghai network. The findings suggest that the alignment of an existing ring line would affect the optimal alignment of the second ring line. In addition, if an outer ring line exists (or is planned to be constructed), the optimal location of the inner ring line might not be its current location. Furthermore, a sensitivity analysis was conducted to test the impact of changes on demand, value of time, and passenger ride cost on the second ring line. Zones that would benefit most from introduction of the second ring line were also determined. Although the case study presents the Shanghai ring lines, the outcomes provide useful information for other cities that are considering the expansion of their transit network with a first or second ring line. Unlike simulations and agent-based models, the model presented in this study is easily transferable to any transit network.