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• Developing a framework to estimate solar PV potential of urban fleet-wide electric buses. • Publicly street-view panoramas, GPS trajectory data and DEM data are used as input. • A case study of Qingdao, China with 547 bus routes, 28,661 street-view panoramas is studied. • Benefits under different driving conditions and operation schedules are evaluated. Transforming the road public transport to run on renewable energy is vital solution to achieve carbon neutral and net zero goals. This paper evaluates the potential of using solar radiation-generated electricity as an auxiliary power supplementary for the battery of electric buses, based on a developed framework that using publicly street-view panoramas, GPS trajectory data and DEM data as input parameters of solar radiation model. A case study of Qingdao, China with 547 bus routes, 28,661 street-view panoramas shows that the solar-radiation electricity generated at noon during the operation accounts for about one-fifth, one-eighth of the total electricity consumption of a bus traveling one kilometer in a sunny day and a cloudy day, respectively. Spatial variability shows significant solar-radiation power generation advantages in newly-launched areas and expressway. The solar power generated in a sunny day can make a bus half of passengers and with air conditioner off at least one extra trip in 2:1 replacement schedule, and nearly close to one extra trip in 4:3 replacement schedule. A correlated relation between the solar-radiation power generation benefit and the operation schedule of electric buses is observed, implying that the high cost of 2:1 replacement schedule for long-distance routes during summer or winter can be reduced. The proposed framework can help us evaluate and understand the feasibility of solar radiation-generated electricity energy of electric bus fleets covering the large-scale urban areas at different times, locations, and weather conditions, so as to support effective decisions at better planning of PV-integrated electric buses. [ABSTRACT FROM AUTHOR]

Electric vehicles are increasingly applied in bus enterprises due to their advantages of environmental protection and low cost. Although there exist many studies on fuel vehicle scheduling, there still lack studies on electric vehicle scheduling in public transit. In this paper, we study a multi-depot electric vehicle scheduling problem (MD-EVSP) in public transit and propose a genetic algorithm based column generation approach (GA-CG) for MD-EVSP. A column refers to the route of a vehicle. First, a heuristic is devised to create a set of initial columns. Then, starting from the initial columns, the column generation approach with a label correcting algorithm is used to generate a set of candidate columns. Finally, a genetic algorithm is devised to select a subset of columns from the column set to construct the final solution. A solution coding scheme is designed to represent a subset of columns, that is, a candidate solution to MD-EVSP. An elite strategy is integrated into the genetic algorithm to improve the global search capability. GA-CG is applied to a real-world problem with three bus lines in Qingdao, China. Experiments show that compared with the branch and price (BP) algorithm and the manual scheduling scheme, GA-CG can effectively solve the problem and its computational time is about 40 times shorter than the BP algorithm. • A connection network model is established for the vehicle scheduling problem. • A genetic algorithm based column generation approach is proposed for the problem. • A heuristic is proposed to generate initial columns to accelerate column generation. • Experiments show that the approach is 40 times faster than the branch and price. [ABSTRACT FROM AUTHOR]

• Proposing a framework to analyze emission patterns of buses and changes in post-COVID-19. • 2056 buses with 1.5 million ridership and 7589 taxis with 0.2 million trips are used for analysis. • 224 social surveys are collected and show a 56.3% ridership reduction in post-COVID-19. • We find buses cannot be "greener" travel modal than cars if ridership reduces by more than 40%. Emission benefits of transit buses depend on ridership. Declines in ridership caused by COVID-19 leads uncertainty about the emission reduction capacity of buses. This paper provides a method framework for analyzing spatio-temporal emission patterns of buses in combination with real-time ridership and potential emission changes in the post-COVID-19 future. Based on GPS trajectory and Smart Card data of 2056 buses from 278 routes covering 1.5 million ridership in Qingdao, China, spatio-temporal emissions characteristics of buses are studied. 7589 taxis with 0.2 million passengers' trips are used for acquiring private cars' emissions to evaluate the emissions difference between buses and cars. Empirical results show that the average difference between buses and cars with 2 persons can reach up to 117 g/km-person during 7:00–8:59 and 115 g/km-person during 17:00–18:59. However, buses have various emission benefits around the city at different periods. A double increase in emissions during non-rush hours can be observed compared with rush hours. 224 online survey data are used to study the potential ridership reduction trend in post-COVID-19. Results show that 56.3% of respondents would decrease the usage of buses in the post-COVID-19 future. Based on this figure, our analysis shows that per kilometer-person emissions of buses are higher than cars during non-rush hours, however, still lower than cars during rush hours. We conclude that when ridership reduces by more than 40%, buses cannot be "greener" travel modal than cars as before. Finally, several feasible policies are suggested for this potential challenge. Our study provides convincing evidence for understanding the emission patterns of buses, to support better buses investment decisions and promotion on eco-friendly public transport service in the post-COVID-19 future. [ABSTRACT FROM AUTHOR]