Photovoltaic and energy storage system (PESS) adoption in public transport (PT) can offer a promising alternative towards reducing the charging and carbon emission costs of transit agencies. However, the quantitative impacts of PESS on operational cost, carbon emission cost, bus scheduling, and energy management in PT remain unclear. This study is performed with the aim of filling this research gap by presenting an impact analysis framework incorporating multisource datasets and a simulation-based optimization approach. A simulation is designed to capture bus scheduling and energy management on a day-to-day transit operation. A surrogate-based optimization method is adopted to determine an optimal PESS configuration for battery electric bus (BEB) charging stations. A case study is performed by introducing PESS at BEB charging stations in Beijing, China, leveraging historical weather and bus operational data. Compared with conventional charging stations, the novel transit system with PESS reduces the annual charging costs and carbon emissions of a single bus route in this case study by 17.6% and 8.8% on average, respectively. The results suggest that the recycling electricity price of PV generation is the key to influencing charging costs and carbon emissions. Battery capacities also impact the scheduling pattern of PESS considerably. Further comparison across five bus routes in Beijing reveals that the deployment locations of PESS are key decisions influencing the operational cost and carbon emissions in PT at the network level when the investment budget is limited. 

Impacts of photovoltaic and energy storage system adoption on public transport: A simulation-based optimization approach

Product placement is a key part of the whole tobacco industry chain. Traditional placement often costs a large manual workload and is strongly influenced by personal experience. To achieve accurate cigarette placement, we propose a set of data-driven intelligent strategies in different segmented markets. An accurate retailer classification cigarette placement algorithm based on the attributes of business circles is proposed, business circle data outside the tobacco database is introduced, and a neural network cigarette placement algorithm with the fusion of business circle features is established. At last, the experimental results show that the average accuracy of the brand (Zhenlonglingyun) based on feature fusion is 84.7% and the brand (Nanjingyinghong) accounts for 90.1%. Through data cleaning and feature fusion, the deep learning model can be trained to generate customized marketing strategies and achieve intelligent and accurate cigarette placement.

Research on a cigarette placement method based on multi-source data and neural networks

The reliability of public transport services (e.g., bus) is a critical factor to be considered among public transport users. This factor is important for the consequences that come with unreliability will significantly increase unnecessary anxiety and discomfort. Such consequences include additional waiting time, late arrival at destinations, and missed connections. The ability of the system to follow the planned schedule and maintain consistent travel time are some of the factors that need to be in focus. In this study, we investigated satisfaction among students who reside in campus towards bus services provided by the university. Factor analysis and structural models were developed to achieve the research objective. Three factors havesignificant relationships with students’ satisfaction. The finding also indicated that bus services are an important requirement of the on-campus students.

Factors Affecting Students’ Satisfaction Towards Bus Services in Campus

The advent of Connected and Autonomous Vehicles (CAVs) and Mobile Internet technologies is reshaping the public transport sector. Autonomous buses are equipped with varying advanced sensors, and hold great promises to enhancing the responsiveness and flexibility of public transit system. In the context of CAVs, transit operators can not only optimize service headway but also adjust bus capacity to meet the time-varying passenger demand. Anticipated benefits of introducing autonomous buses to the existing transit systems include safety improvement, driver cost reduction, and optimal routing. Bus electrification is another global trend to replace traditional diesel buses for energy savings. Electric buses are advantageous to both operators and passengers due to low greenhouse gas emission, maintenance cost, as well as noise pollution. In recent years, demand responsive public transport (DRPT) services (e.g. customized bus, microtransit) receive huge success thanks to the development of Mobile Internet. Unlike traditional bus services or the fixed-route transit service that relies on passive recipients, fixed stops, and schedules, DRPT can provide personalized service for specific clients through interactive information platform (Internet or smartphone). The aforementioned new types of public transit services significantly improve service quality, reduce energy consumption, and ultimately attract more ridership. To fully explore the benefits of personalized, electric and autonomous transit systems, new analytical models and data-driven methods for transit planning and operation are needed. The authors have selected 15 articles for review in this special issue. A summary of these articles is outlined below.

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Xiaoyue Liu

Papers

Public Transit Planning and Operation in the Era of Automation, Electrification, and Personalization