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This paper proposes a comprehensive architecture for a Mobility-as-a-Service (MaaS) platform, focusing on service browsing, ticketing, and payment, with a specific emphasis on integrating MaaS into bus payment systems. The proposed architecture aims to enhance user experience and convenience in accessing and paying for various transportation services while providing support for MaaS startups, offering a novel approach to MaaS, addressing challenges related to service discovery, ticketing, and payment through a unified system. The proposed MaaS platform holds the potential to improve mobility, reduce congestion, and promote sustainable transportation. [ABSTRACT FROM AUTHOR]

Driven by the massive number of connected vehicles and the stringent requirements of data-intensive applications, logistics transportation systems have evolved to fully comprehend its effectiveness and quality to meet public transportation needs. As a result, to meet public transportation requirements and analyze the data efficiently at the edge of the networks, an advanced artificial intelligent technique needs to be introduced to make the transportation system intelligent by supporting efficient decision making, intelligent traffic control, and intrusion and misuse detection.Motivated by the challenges mentioned above, in this paper, we develop a logistic agent-based model for analyzing public transports such as cars, bus or trains in the intelligent transportation system. The intelligent logistic framework is built on a parallel neural network structure, known as a Swarm-Neural Network (SWNN). The proposed SWNN model analyzes the sensory data and recognizes the public transportation at the edge of the networks. The SWNN model is constructed so that it fits within the intelligent logistic transportation framework, and the proposed model shortens the transit time of every small-scale logistics delivery to its destination. The performance of the proposed SWNN model is evaluated using a standard TMD dataset, where the SWNN model is trained using data, retrieved multiple sensors such as accelerometer, gyroscope, magnetometer, and audio sensors. The features of the sensory data are extracted based on a 5-s time interval. The performance of the proposed SWNN model is studied over various standard machine learning techniques such as Random Forest, XGBoost, and Decision Tree. As per the simulation results, the proposed technique achieves 78–98% accuracy over a real-time dataset's different sets of features. [ABSTRACT FROM AUTHOR]

The adoption of plug-in electric buses are seen as a promising alternative to reduce greenhouse gas (GHG) emissions in the public transport system. Although from an operational point of view this type of vehicles are zero-emissions, the upstream phase contribution can be considerably high in regions with high carbon grid intensities. This paper presents a simulation study to evaluate the energy consumption and the corresponding well-to-wheels (WTW) CO 2 emissions of conventional diesel, parallel hybrid and battery electric buses. The test case is the island of Tenerife, an isolated power system highly dependent on fossil-fuels for power generation. Validated models of the aforementioned propulsion architectures were simulated over five representative cycles, covering city and intercity lines with noticeable differences in terms of driving behaviour, distance and gradient. Although battery electric buses present energy consumption reductions of 70% compared to conventional powertrains, the large carbon grid intensity of the island has a negative effect on their potential to reduce WTW CO 2 emissions. The results evince that parallel hybrids provide the largest benefit to abate WTW CO 2 emissions, reaching a reduction figure of 38% compared to its conventional diesel counterpart. [ABSTRACT FROM AUTHOR]

The availability of alternative fuels for transportation means using renewable energy is an essential factor for achieving sustainable development. Natural gas use is increasing, and it ranks as one of the most often consumed alternative fuels for public transportation and is leading to improved air quality in urban areas, enabling cleaner production in the transport sector. The investment and maintenance costs for buses fuelled by compressed natural gas (CNG) are higher than for diesel buses. Notably, the use of CNG buses can reduce fuel costs. These factors have a significant influence on the financial effectiveness of deploying CNG buses for public transport. Replacing diesel buses with CNG buses in urban transport reduces the emission of toxic substances and greenhouse gases and contributes to reducing the negative impact from the transportation sector on the environment. Fuelling the buses with a renewable energy source also results in reduced external costs, a crucial factor in determining the economic effectiveness of investments in the transport sector. This paper considers the costs and benefits associated with the purchase and use of CNG buses for public transport and presents the results of an evaluation of their financial and economic effectiveness. Additionally, the benefits resulting from reducing the negative impacts of the public transport sector on the environment, which is a crucial factor for cleaner production in the transport sector and fulfilling the aims of the sustainable development strategy, are calculated. On this basis, economic policy recommendations were formulated. Image 1026 • The costs and benefits of using CNG buses have been identified. • The cash flows of the use of CNG and Diesel buses were compared. • The econometric model for forecasting the operating costs was created. • The effectiveness of using the CNG buses in public transport was assessed. • Recommendations for economic policy were formulated. [ABSTRACT FROM AUTHOR]

The public transport market in most global cities is dominated by diesel buses, which can have significant adverse impacts on the environment and human health. To reach carbon neutrality, many public transport operators are attempting to replace their existing bus fleets with electric buses (EBs). This paper develops a generic bus replacement model to help public transit (PT) operators progressively renew their existing bus fleets so as to reach particular electrification goals within a given planning horizon. Unlike existing studies, our model more comprehensively considers the external costs, which include external climate costs, external health costs and used battery recycling costs/profits. Furthermore, by incorporating different incentives, variants on the model can be adopted to investigate how different government incentives can affect the replacement behaviors of PT operators. Finally, our proposed approach is applied to real-world data from a bus operator in Singapore which is aiming to achieve a 100% cleaner energy bus fleet within 20 years. The experiments indicate that an EB subsidy can effectively accelerate the electrification process, and a 25% EB subsidy could achieve full electrification eight years earlier than if no EB subsidy were applied. Moreover, we explore how the diesel tax and diesel bus (DB) tax affect EB replacement. Our experiments suggest a 10% diesel tax rate can achieve good results but imposing a DB tax will not encourage PT operators to purchase EBs earlier. • A novel model for the bus fleet replacement problem is proposed. • Various external costs that borne by the society and environment are considered. • Our approach evaluates different policy incentives. • Our real case study provides managerial insights for PT operators and policymakers. [ABSTRACT FROM AUTHOR]

It is essential to take into account the service quality assessment made by the passengers of a public transportation system, as well as the weight or relative importance assigned to each one of the attributes considered, in order to know its strengths and weaknesses. This paper proposes using Artificial Neural Networks (ANN) to analyze the service quality perceived by the passengers of a public transportation system. This technique is characterized by its high capability for prediction and for capturing highly non-lineal intrinsic relations between the study variables without requiring a pre-defined model. First, an ANN model was developed using the data gathered in a Customer Satisfaction Survey conducted on the Granada bus metropolitan transit system in 2007. Next, three different methods were used to determine the relative contribution of the attributes. Finally, a statistical analysis was applied to the outcomes of each method to identify groups of attributes with significant differences in their relative importance. The results show that statistical significant differences exist among several categories of attributes that have a greater or lesser impact on service quality and satisfaction. All the methods agree that Frequency is the most influential attribute in the service quality, and that other attributes such as Speed, Information and Proximity are also important. [ABSTRACT FROM AUTHOR]

• Underground circular line selection among several alternatives, in Greater Cairo city. • General and adaptable methodology for large scale transit networks. • The method minimizes the number of passengers' transfers and maximizes the service. Connectivity is a significant problem in large-scale transit networks because the number of transfers required to conduct a trip is considered a discomfort by transit users. This paper presents a practical solution for an underground metro line planning problem by integrating existing bus and metro networks into a single connected transit network. The proposed method aims to obviate the usual combinatorial complexity when solving a transit route design problem. It aims to increase the overall transit system connectivity by selecting a consistent and non-demand-oriented criterion for the design. The metro lines are designed by minimizing passenger transfers through the transit network according to predefined demand node pairs. The design scheme offers a set of ring route alternatives for a sizeable case study in Greater Cairo. The case study selected sixteen traffic analysis zones, an existing metro network consisting of three main lines (113.6 km long), and twelve main bus lines (487.7 km long) for analysis. TransCAD software was used as the basis for coordinating the stations and lines of both the bus and metro systems. Subsequently, a passenger transfer counting algorithm was implemented to determine the number of transfers required between stations from each origin to each destination. A passenger origin–destination transfer matrix was created using the NetBeans integrated development environment to help determine the number of transfers required to complete trips on the transit network before and after proposing the new line. Based on the evaluation, the ring lines were highly efficient at significantly decreasing passenger transfers between stations with the minimum construction cost. This study will be of value during the strategic stages of the transit line design and will assist in rapidly generating initial solutions when certain demand information is unavailable. [ABSTRACT FROM AUTHOR]

• High-pressure fuel injection lines loaded with cyclic dynamical stress. • Reduced content of carbon on the outer surface. • Decarburization causes decrease of fatigue strength. • Fracture occurred due to fatigue of the material. This paper presents the results of the failure analysis of high-pressure fuel injection lines of a diesel engine that caused fire on the public transportation bus. Fuel injection lines had been in operation for 18 months, and two of them were cracked at the point where they were connected to the engine injector. These damages caused fuel leaks and finally fire on the bus which spread into the passenger cabin and caused significant damage. The failure mechanism was analysed by both visual and SEM inspection of the fracture surface, identifying fatigue as the major cause of the final failure. Microstructure analyses, microhardness measurements together with chemical analysis were performed in order to check any possible deviation in the material structure and/or composition. The results showed that fatigue failure occurred due to the decarburization of the outer surface of the high-pressure lines. [ABSTRACT FROM AUTHOR]

Group decision-making on a public service problem is often biased by untrustworthy responses of the participants, which affects negatively the outcome of the procedure. To eliminate this problem, the state-of-the-art methodologies apply fuzzy sets, generally within the frames of hybrid methodologies. The objective of the recent paper is to introduce a new hybrid model based on picture fuzzy sets and linear assignment and its first real-world application on a public transport development problem. The advantage of the proposed methodology is that it simultaneously considers the indeterminacy level of respondent evaluations on decision alternatives, and applies linear assignment to avoid subjectivity in responses. With the aim of testing the reliability of the new methodology, the results are compared with the generally applied intuitionistic fuzzy Technique of Order Preference Similarity to the Ideal Solution model. The comparative results have shown that the final outcomes of the proposed method are very similar to the well-proven reference technique, thus the hybrid picture fuzzy AHP-linear assignment model has been validated. • Integrating the Picture Fuzzy AHP and Linear Assignment models. • Using the proposed approach for evaluating public transportation in Budapest. • Considering hesitant scoring and synthesizing different stakeholder group opinions. • Expressing decision-makers' judgments using picture fuzzy linguistic terms. [ABSTRACT FROM AUTHOR]

Customized Bus is a new mode of Internet-supported public transportation. It is regarded as one of the major strategies to reduce the usage of private cars and mitigate greenhouse gas emissions from road traffic. For a customized bus system, a dynamic bus line planning system based on the demand can largely improve the performance and promote the public acceptance of customized bus service. This paper introduces a method to generate planning suggestions for bus lines and stops based on massive demand data. A link network is generated from the input to represent the sharing route of the demand. With community detection, the link network is segmented into communities with similar travel routes. By examining the core-peripheral structure and matching the core part of communities with the road network, the customized bus lines are generated. Boarding and alighting hotspots are identified as the suggestion for customized bus stops. The methodology is tested by using mobile phone data in Tokyo. With the input of one-day sample, the algorithm can generate the result in approximately 1 min and extract 29 bus lines. According to the shape and spatial location of the bus lines, three types of bus lines serving different travel patterns are classified: radiation type lines, ring-type lines, and suburban lines. Analyzing the emission reduction potential of the extracted bus lines manifestes that bus line planning of the proposed method has the potential to relieve emission pressure on urban expressways and to reduce approximately 13% of road traffic emission. Image 1 • A data-driven approach is proposed for the systematic planning of customized bus. • 1.4 million car trajectories are used to simulate dynamic input for the method. • Dynamic bus lines are generated from spatial distribution of sharing trajectories. • Suggestion of bus stops are extracted from boarding and alighting hotspots. • Potential demand and emission reduction of customized buses is analyzed. [ABSTRACT FROM AUTHOR]

The introduction of hybrid electric technology for urban public transportation can be considered a valuable stepping stone to low-carbon mobility. Using the contingent valuation method, a sample of 427 respondents were asked about their willingness to pay for the air pollution reductions associated to a scenario of gradual introduction of hybrid electric buses in Valencia, Spain. Results show that 67% of the respondents are willing to pay extra for the adoption of this electric hybrid technology, being the mean willingness to pay € 0.33 per single bus fare. This would imply a 22% hypothetical increase in current bus fares and shows that public acceptance is critical for the successful introduction of these new low-emissions vehicles. Further, this paper differs from previous research in that it addresses the issue of heterogeneity among respondents. Thus, using latent class analysis, based on questions about environmental concerns, we identify three different classes of respondents: high, medium and low environmentally concerned. Mean willingness to pay for each class ranges from €0.25 to €0.38. Then we analyze willingness to pay determinants through the estimation of a Tobit model for each class of respondents. This reaffirms the existence of heterogeneity and accordingly suggests that treating the population as a single homogeneous group of respondents, as is common in the existing literature, may lead to biased parameter estimates and flawed decision-making regarding climate change action. Image 1 • Public acceptance of hybrid buses is analyzed in a Contingent Valuation scenario. • Preference heterogeneity is investigated using a Latent Class Model. • Results show a high level of heterogeneity among respondents. • Mean willingness to pay for each class of respondent is estimated. [ABSTRACT FROM AUTHOR]