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Autonomous-rail Rapid Transit (ART) is a new type of road public transportation with an automatic driving function, and it is one of the main places for epidemic spread. Scientific wearing of masks has been proven to be an extremely effective way to prevent an epidemic. However, drivers wearing masks make it difficult for traditional driver alert detection methods based on facial information to operate effectively. This paper proposes an ART driver alert detection system based on multimodal information fusion and deep learning, which can be applied to drivers wearing masks. The detection system is mainly divided into three modules: data acquisition module, preprocessing module, and vigilance detection module. The data acquisition module collects real-time face thermal imaging data, and environment temperature information of 13 people in the vigilance and fatigue state of the ART simulated driving environment. The preprocessing module proposes a face thermal imaging key information extraction method based on the constraints between Yolov5 and thermal image interframe constraints and extracts the mean temperature information and the hot voxel information (HV) of the eyes, forehead, and mask areas. Then the extracted information and the environment temperature information are transmitted together to the vigilance detection module for learning and classification. The vigilance detection module proposes a lightweight hybrid-attention optimization network, and this paper extracts training sets and testing sets from the data of 13 subjects. And the training set data is enhanced. Each sample is 90 frames. The maximum accuracy rate of the testing set reaches 99.57%. The feasibility of the detection system proposed in this paper is proved, which makes the thermal imaging and attention model show great potential in the application of traffic safety operation and maintenance. • A contactless vigilance detection method is proposed for ART drivers with face masks. • The multi-modal fusion method uses facial thermal image and environmental data. • A lightweight network model that integrates hybrid-attention mechanism is proposed. • A method for extracting face key information from thermal images is proposed. • The testing set shows that the system is feasible and the accuracy rate is 99.57%. [ABSTRACT FROM AUTHOR]

Subgroup discovery (SD) aims at finding significant subgroups of a given population of individuals characterized by statistically unusual properties of interest. SD on event logs provides insight into particular behaviors of processes, which may be a valuable complement to the traditional process analysis techniques, especially for low-structured processes. This paper proposes a scalable and efficient method to search significant SD rules on frequent sequences of events, exploiting their multidimensional nature. With this method, it is intended to identify significant subsequences of events where the distribution of values of some target aspect is significantly different than the same distribution for the entire event log. A publicly available real-life event log of a Dutch hospital is used as a running example to demonstrate the applicability of our method. The proposed approach was applied on a real-life case study based on the public transport of a medium size European city (Porto, Portugal), for which the event data consists of 133 million smartcard travel validations from buses, trams and trains. The results include a characterization of mobility flows over multiple aspects, as well as the identification of unexpected behaviors in the flow of commuters (public transport). The generated knowledge provided a useful insight into the behavior of travelers, which can be applied at operational, tactical and strategic business levels, enhancing the current view of the transport services to transport authorities and operators. • Significant subgroup discovery rules on sequences of multidimensional events. • Process discovery and conformance checking on low-structured processes. • Real-life case study based on smartcard travel validations of a transport network. • Characterization of mobility flows over multiple aspects. • Identification of unexpected behaviors in the flow of commuters. [ABSTRACT FROM AUTHOR]

This paper examines the economic and policy implications of the charging, network and auxiliary infrastructure required for a fully electrified metropolitan Melbourne bus network using overnight depot charging. We introduce the concept of minimum and maximum fleet charging capacities, as well as a charging infrastructure augmented total cost of ownership that is faced by bus fleet owners. Our analysis provides policy makers with a range of viable fleet charging capacities and a cost component breakdown of public bus fleet electrification using battery electric vehicles. We find that a minimum of half the fleet's rated charging capacity is needed to maintain operation, which provides a lower bound for network and charging infrastructure costs. Considering the augmented total cost of ownership (per km), charging, network and auxiliary infrastructure costs contribute 10–19%. A gradual transition to battery electric buses over 12 years yields further TCO savings of between 4.6 and 5%. We recommend streamlining the network augmentation process to reduce uncertainty and project delays. • Calculation of viable charging capacities to operate a metro BEV bus fleet • BEV buses operate using an overnight charging strategy with real world schedules • Fleet can continue operating with half its rated charging capacity • TCO is augmented with charging and network infrastructure costs • Charging, network and auxiliary infrastructure contributes 10–19% to the TCO [ABSTRACT FROM AUTHOR]

Modern technologies provide new opportunities for the industry but raise the expectations of the customers as well. This is valid also for the postal, courier, and logistics industries. The biggest challenge for the companies in the field is to improve the efficiency of the final phase in the transfer of shipments – last-mile delivery. A contemporary solution relates to collaboration between delivery companies and citizens who are willing to perform the delivery tasks as an additional activity to their established job or routine. Such a concept is known as crowdsourcing. There are several approaches within crowdsourcing, for example, transportation by car, utilizing public transport, etc. To make an appropriate choice, multiple criteria should be considered. This paper aims to propose an original methodology for solving the explained multi-criteria decision-making problem. The proposed framework is based on the application of the Sugeno–Weber nonlinear functions in a fuzzy environment. The application of the fuzzy Sugeno–Weber weighted assessment methodology showed that the proposed methodology has adaptability, a high degree of generalization, and stability of results. This enables the applicability of the methodology to various tasks from a broad spectrum of areas, which represents an additional value. A real-life case study is provided to solve the prioritization of crowdsourcing models in suburban municipalities of Belgrade, Serbia. The available alternatives and the most important criteria for the crowdsourced delivery model are identified. All criteria are divided into four groups, which define the dimensions of sustainability, including the technical aspect. The results of the conducted research indicate a high level of convenience in using one's car or daily bus lines for crowdsourced delivery to optimize the entire process of shipment transfer in the observed territory. [ABSTRACT FROM AUTHOR]

• A vibration prediction model of long-span steel truss cable-stayed bridge was established, and validated by test results. • The vibration reduction mechanism of damping pad floating slab (DPFS) on the long-span steel truss cable-stayed bridge was analyzed in this paper. • The vibration reduction effect of the damping pad floating slab (DPFS) on the long-span steel truss cable-stayed bridge was discussed in this paper. With the development of urban rail transit, long-span steel truss cable-stayed bridges have become increasingly favored. Therefore, train-induced bridge-borne vibration and noise are occurring more often. The damping pad floating slab (DPFS) has been widely used as an effective vibration damping track structure; however, there is a lack of research on the vibration mechanism and effects of the DPFS for long-span steel truss cable-stayed bridges. This study focuses on the steel truss cable-stayed bridge in urban rail transit. First, the theoretical model of the train-track-bridge coupling interaction is established in the frequency-domain (the track structure is DPFS, and the interaction of multiple wheels is considered). Using this model, the wheel–rail dynamic force, force transmission rate of the track, and the force transmitted to the bridge are calculated. Then, the vibration prediction model of the long-span steel truss cable-stayed bridge is established using the plate-beam finite element (FE) method and statistical energy method. The force transmitted to the bridge is applied to the vibration prediction model to obtain the vibration transmission characteristics of the typical plate of the bridge. The prediction model is verified by comparing the obtained results with the field test of a long-span steel truss cable-stayed bridge, and the damping mechanism of DPFS track is discussed. Finally, the force transmitted to the bridge is calculated based on the embedded-sleeper (ES) track structure (the ES has no vibration damping facilities). The values of the overall vibration level are calculated from the prediction models using ES and DPFS, and the vibration reduction effect of DPFS is studied by comparison with the ES. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) proliferation relies on robust charging infrastructure deployment and operational advances that require cooperation among multiple stakeholders invested in supporting growth in EV adoption. While growth in EV adoption is influenced by the availability of charging infrastructure, the decisions necessary to successfully roll out such infrastructure are multi-dimensional and cross individual stakeholder's comfort zone. In this paper, we present experiences and lessons learned of a multi-year multi-institution effort to deploy advanced charging infrastructure for increasing EV adoption in the state of Utah. Three distinct charging infrastructure cases are studied in this paper: residential charging, public charging in electrified highways, and electrified public transportation through electric buses. Each of these charging infrastructure present unique challenges and decision criteria for different stakeholders involved in the process. This paper highlights the role of decision makers in addressing and overcoming the challenges to EV proliferation and provides charging infrastructure utilization data to support such strategies. [ABSTRACT FROM AUTHOR]

Abstract In this paper we will consider generalizations of the class of so-called windmill graphs, which were recently introduced by Estrada [6]. A windmill graph W (η , k) consists of η copies of the complete graph K k , with every node connected to a common node. Estrada [6] showed that the clustering coefficient and the transitivity index of windmill graphs diverge, when the graph size tends to infinity. In addition [6] studied the spectra of the adjacency and the Laplacian matrices of these graphs. In this paper we will generalize the family of windmill graphs in three ways. We will study properties for all three types of generalized windmill graphs. In particular we will focus on the behavior of the two clustering metrics. We will quantify the difference between the two metrics, under various conditions. In addition, we give the spectra of the adjacency and the Laplacian matrices of these graphs. We will also derive analytic expressions for several other graph metrics, such as average path length, heterogeneity index and a variety of robustness metrics. We also show how the generalized windmill graphs can be used to construct pairs of non-isomorphic graphs with the same number of nodes and links. Finally, we will show how generalized windmill graphs occur quite naturally in the study of public transportation networks. [ABSTRACT FROM AUTHOR]

With the global acceleration of the carbon-neutral process and the widespread adoption of electric buses in various countries, the placement of charging stations plays a crucial role in promoting low-carbon development in public transportation systems. This paper proposes a two-stage approach for siting electric bus charging stations considering future-present needs. Initially, perspectives on future and current needs, a community discovery model is constructed using bus space-time swipe card data to identify the municipal bus travel link network. This model considers the OD distribution of bus passenger flow, which is essential for determining the line network and capacity layout. Additionally, a bus first and last station hotspot identification-Voronoi diagram model is established based on the service area partition of bus charging stations, incorporating the weight of charging demand. The model assesses the aggregation of charging demand in each service area using demand aggregation and spatial aggregation indicators. It utilizes the current bus charging requirements aggregation pattern to determine the siting of bus charging stations in the sub-regions, resulting in a data-driven two-stage method for siting electric bus charging stations considering future and current demand. The city is divided into 11 bus charging station subzones using the community discovery model, and sites are selected within each subzone. The results indicate that the demand aggregation degree has increased to some extent in 10 service areas compared to the initial sub-district and an overall increase of 22.79% for the entire city, from 0.487 to 0.598. Among them, 8 service areas have a demand aggregation degree greater than 0.5, indicating that charging demand in most sub-districts is concentrated in high-value aggregation areas. The spatial aggregation degree of all service areas is less than 1, with a decrease in the spatial aggregation degree of 7 service areas and an overall decrease of 11.47% for the entire city, from 0.680 to 0.602. Most service areas experience a decrease in spatial aggregation degree while the demand aggregation degree increases, optimizing the structure of the service areas and reducing the time cost of charging the buses. The model demonstrates good applicability to charging stations and offers an effective approach to siting them. • Use of residents' travel data to delineate initial service areas for charging stations. • Consider the longevity of charging station use in terms of the stability of the spatial distribution feature of travel OD. • Introducing demand aggregation and spatial aggregation to measure the aggregation structure of charging demand. • Site selection based on the distribution of charging demand hotspots at bus route terminals. • Considered the implementability of bus charging station deployment. [ABSTRACT FROM AUTHOR]

Micromobility modes such as scooters, e-scooters, skateboards, or hoverboards has recently emerged as part of the urban landscape. In this paper, we analyze the use of modes of micromobility for commuting. We distinguish between monomodality (commuters using one mode of micromobility only) and multimodality (commuters using micromobility as a complement or substitute to other modes of transport). We apply non-parametric ordered methods to a survey that was conducted in 2018 on mobility users in four European countries. The survey gathered 4873 observations from commuters in France, Germany, Spain, and the United Kingdom (UK). Micromobility commuting is marginal in all four European countries. The sociodemographic characteristics of micromobility commuters are homogeneous and concern mainly male, young, and urban commuters. We find that travel habits account for a large share of the variability explained by the model. Germany has a low level of multimodality, whereas the UK practices complementarity-oriented multimodal commuting. Overall, our results bring new insights showing that micromobility is used as a (partial) substitute to urban transit systems for short distances and as a complement for longer commuting trips made by train. These emerging patterns of commuting require better modal integration between micromobility and public transport, and a more sophisticated design of transport infrastructures. [ABSTRACT FROM AUTHOR]

Transport is one of the sectors of the energy consumption where the presence of renewable energies is lower and its increase is hard to achieve especially in countries that import all the fuels that they consume. This paper addresses the progress that Portugal is making toward a more sustainable transport sector. A near- and medium- term scenario analysis is performed, based on the energy consumption in the past and on a detailed evaluation of the trends for renewable energy in the transport sector. The near-term scenarios aim at portraying the conditions that allow the country to fulfill the target of 10% renewable share in transportation established by the European Union. The medium-term scenarios highlight the implications, in terms of emission reduction and of energy independence, deriving from the use of electric vehicles and natural gas, synthetic and fossil, as alternatives to liquid fuels. The results show that electric vehicles would substantially reduce the total energy consumption and the greenhouse gas emissions, while the promotion of routes for synthetic natural gas would contribute to taking advantage of local resources. The paper also provides some insights about the design of future policies to build a more sustainable transport sector. [ABSTRACT FROM AUTHOR]

This paper investigates the theoretical capacity of the Swiss peri-urban dwelling stock to meet energy efficiency requirements through incremental scenarios. The notion of “typical dwelling” allows the assessment of housing energy efficiency at the country scale. The “2000-W society” concept and federal policies provide the assessment framework of the daily-mobility and housing operational energy demand along with embodied energies of the dwelling stock. The typical dwellings’ current performance for several territorial entities highlight the extent of the challenge the peri-urban housing stock is facing today. Therefore, the paper investigates which conditions would allow the peri-urban typical dwelling to meet “2000-W society” intermediary targets. Eight incremental scenarios present theoretical improvements of mobility and housing. They assume an evolution of social practices and individual behaviours, as well as the development of improved technologies. Results show a drastic reduction of primary non-renewable energy (PNRE) demand and greenhouse gas (GHG) emissions for both mobility and dwelling-related consumption. The main findings are that the current MINERGIE-A label is sufficient to build energy efficient buildings, but the current mobility practices remain very far from targets. An optimization of trips and a broader recourse to low carbon conveyances are required to reduce the overall environmental footprint. [ABSTRACT FROM AUTHOR]

• Solves the practical problem of fleet allocation for disadvantaged travelers with limited public resource. • Designs an algorithm to maximize the positive social impact. • Performs a case study considering the geographic restrictions to test the developed method. The COVID-19 lockdown has reduced public transportation service to the disadvantaged and disabled people who urgently need adequate mobility to obtain essential suppliers. This paper aims to improve the life quality of people with disabilities and elderly people by addressing social exclusion, accessibility, and mobility issues. Demand responsive transport services are frequently offered in the context of door-to-door transportation of the elderly and persons with disabilities. We study and compare two frameworks. We apply both Sample average approximation (SAA) and Rolling Horizon (RH) to optimize a car sharing system for the total cost, including initiation cost and operation cost after fleet size is determined. The model is implemented with given geographic conditions and other local information to be tailored for specific applications for local communities. Given that no historical data is available, random sample data is generated to simulate expected demands. We consider three types of probability distributions for daily demand data, and the results generated using three different distributions are being examined and compared. The research shows that the demand data following a normal distribution results in the minimum total cost. Additionally, we study the impact of several factors on total cost, including demand fulfillment rates and operation hours. Our results suggest that the impact of fulfillment rate on fleet size is exponential after a threshold under all three types of daily demand data, and extended operation hours can significantly reduce the total cost. Finally, the paper provides applicable frameworks for city planners, NPOs, and policymakers to better allocate limited resources to implement the carsharing system when little to no historical travel information is available for low-density population areas. It is anticipated that the outcome from this research would benefit disadvantaged and disabled travelers during COVID-19 or similar difficult situations in the future. [ABSTRACT FROM AUTHOR]

Air pollution not only affects residents' work and life, but also affects residents' health, which has negative effects on residents 'subjective well-being. In order to improve air quality, the Chinese government is making vigorous efforts to control air pollution. The purpose of this paper is to explore the impact of air pollution control on urban residents' happiness based on the micro-level survey data conducted in air pollution transmission channel cities. Firstly, the grounded theory is used to obtain the dimensions of the influence of government governance on residents. Then, the impact of these factors on residents' happiness in the context of air pollution control is examined. The empirical results of our study indicates that: (a) living experience was the main factor affecting residents' happiness, and order of effects through three ways on happiness are economic punishment for burning straw > uninterrupted sprinkler in city > prohibition of coal-fired heating; (b) the impact of green travel and public transportation pattern on residents' happiness is not significant; (c) the positive impact of heating supply pattern on residents' subjective well-being is mainly driven by people's general acceptance of "coal to gas" policy; (d) the promotion influence of fiscal policy on residents' subjective well-being is mainly achieved through subsidies for electric vehicles; (e) a good job security is one of the reasons for general improvement of residents' subjective well-being in the process of air pollution control. Image 1013 • The impact of air pollution control on urban residents' happiness is analyzed. • The micro survey data is conducted in air pollution transmission channel cities. • The grounded theory is used to find the dimensions affecting residents' happiness. [ABSTRACT FROM AUTHOR]

• A case study based on the national BCycle bike-sharing program was presented. • The bike usage patterns were compared among different membership groups. • The spatio-temporal trends of empty and full bike stations were examined. • The current system configuration was optimized under three hypothetical scenarios. • This study has important implications to other cities in the BCycle program. Many cities around the world have integrated bike-sharing programs into their public transit systems to promise sustainable, affordable transportation and reduce environmental pollution in urban areas. Investigating the usage patterns of shared bikes is of key importance to understand cyclist's behaviors and subsequently optimize bike-sharing programs. Based on the historical trip records of bike users and station empty/full status data, this paper evaluated and optimized the bike-sharing program BCycle in the city of Boulder, Colorado, the United States, using a combination of different methods including the Potential Path Area (PPA) and the Capacitated Maximal Covering Location Problem (CMCLP). Results showed significantly different usage patterns between membership groups, revealed diverse imbalance patterns of bike supply and demand across stations in the city and provided three system upgrading strategies about maximizing the service coverage. This case study is committed to future energy conservation and sustainable energy systems nationwide and ultimately worldwide, by holding immerse potential to adapt the resulting optimization strategies to the cities with a similar urban context across the United States, as well as more emerging bike-sharing programs in other countries, such as China. [ABSTRACT FROM AUTHOR]

• A agent-based simulation optimization model is proposed to optimize the release of passenger guidance information in a rail network. • A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed. • A case study of Beijing subway is carried out with the large-scale smart card data. The passenger flow guidance is an effective demand management strategy to alleviate the excessive congestion in the urban rail transit network. In order to determine the scope and the timing, a simulation-based optimization model is proposed to optimize the release of passenger flow guidance information in the rail transit network in this paper. In the optimization model, we mainly focus on three aspects namely; where, when and what type of the guidance information should be released to the passengers. In the simulation model, the passenger choice behavior is captured by the agent-based simulation method, which responses to the congestion and the guidance information. Based on this, the dynamic passenger flow distribution can be derived. Furthermore, the adoption rate of the displayed guidance information on passenger information system as well as its impact on passenger travel behavior are also considered in the model. A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed to solve the proposed simulation-based optimization model. Finally, a case study of Beijing subway is carried out with the large-scale smart card data. The numerical study shows that the passenger flow demand affects the guidance effect significantly and the best guidance effect can be met with sufficiently high passenger flow demand. And the guidance rate is also found to affect the guidance results. The results also show that the proposed model can provide a detailed guidance scheme for every station at selected time intervals. The results show that the dynamic releasing scheme can save up to a total of 46,319 min in passenger travel time during a single guidance period. [ABSTRACT FROM AUTHOR]

Based on semi-structured interviews with key stakeholders in Nanhai, this paper adopts an analytical framework to examine how a combination of infrastructure-pull, supply-push and demand-pull initiatives implemented by a small district government in southern China were able to facilitate socio-technical transitions to the public transport decarbonization through the establishment of hydrogen refuelling infrastructures, the adoption of fuel cell electric vehicles (FCEVs), and the development of production network on FCEVs. With neither massive investment from the central government nor captive linkage with global automakers, Nanhai out-performs other automotive powerhouses in China by accounting for 9.85 percent of FCEVs and 4.59 percent of all operating hydrogen refuelling stations in China, despite its land area only accounting for 0.01 percent of the country. The provision of hydrogen infrastructures and other pro-active local government initiatives (including financial incentives) facilitate the development of a competitive regionalized production network of about 90 (privately-owned) fuel cell electric buses makers and parts suppliers around the Xianhu Hydrogen Valley in Nanhai. This finding enhances our understanding on effects of (local) state policies on the socio-technical transitions to public transport decarbonization and the long-term development of hydrogen economies in other countries. [ABSTRACT FROM AUTHOR]

Co-modality transportation aims to integrate passenger and freight flows into a single system by sharing public transport between passengers and freight. This paper proposes a crowdsourced co-modality transportation system (CCTS) for enhancing the practical implementation of co-modality transportation in urban areas. Firstly, empirical investigations are conducted, including semi-structured interviews with industry practitioners and online surveys with passengers, to collect system design considerations. Secondly, an overview of the crowdsourced co-modality management platform (CCMP) is presented by highlighting three key components: co-modality services, information infrastructure, and decision support system. Thirdly, we formulate the parcel assignment problem in the CCTS and examine the factors that influence the performance of the CCTS. The empirical investigations suggest that crowdsourced passengers care about the potential risks and the degree of operation difficulty in crowdsourced activities but do not care about privacy issues. In addition, the experimental studies show that crowdsourced co-modality is more feasible and promising if more passengers are willing to provide crowdsourcing services. [ABSTRACT FROM AUTHOR]

Cities around the world suffer from congestion and pollution caused by road traffic. Intermodal transport, which integrates multiple modes in a single journey, is a promising concept for shifting commuters away from private cars and making urban traffic more sustainable. Urban intermodal transport requires reliable route planners—i. e., planners identifying routes that have a high probability of being fast—because reliability matters for commuters. Designing such reliable route planners for the comprehensive set of modes in modern, real-world urban transportation networks is complex. Challenges arise due to the uncertainties inherent in travel times, transfer connections, availabilities of shared vehicles, and the location of shared vehicles. In this paper, we overcome these challenges by providing a reliable route planner that includes the comprehensive set of modes present in real-world urban transportation networks, including cars, trains and buses, station-based bikesharing, free-floating shared e-scooters, as well as walking. As a second contribution, we provide an in-depth analysis of the potential travel-time savings that such a reliable planner generates. Thereby, we generate new knowledge regarding the effect of several parameters and route characteristics on travel time savings—such as the required level of on-time arrival probability, traffic congestion, trip complexity, e-scooter availability, route costs, and more. Our findings are generated based on a large-scale empirical case study in the city of Hamburg covering more than 320,000 travel connections between 730 locations. Reliable planning yields in our base scenario average travel time savings of 8.9% in comparison to a conventional planner. For single routes, these savings reach up to 37.1%. We furthermore find that time savings increase with the required level of on-time arrival probability and the level of traffic. Moreover, the savings increase with the number of rides included in a trip; however, trip distance has no clear effect. Constrained cost budgets of commuters can considerably prolong travel times, whereas the effect of e-scooter availability is comparatively small. For transportation research, these findings extend current knowledge on travel time savings enabled by reliable planning. For transportation practice, our methodology can provide the basis for implementations in routing information systems, such as Google Maps, as well as planning decision support systems for transportation authorities to evaluate interventions, such as investing in the reliability of public transit or adding buffer times to schedules. • Design of a reliable route planner for transportation networks with comprehensive sets of modes. • In-depth analysis of potential travel-time savings of reliable route planning systems. • Average travel time savings of 8.9%, reaching up to 37.1% for specific routes. • Savings increase with number of rides, on-time arrival probability, cost budgets and e-scooter availability. • Methodological basis for implementations in routing decision support systems. [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]

In recent years, China has been gradually consolidating the establishment of Low Carbon Communities (LCC) to minimize the emission of CO 2 and other greenhouse gasses. Guangdong Province, as one of the low-carbon pilot provinces, has also paid great attention to the building of LCC. However, currently Guangdong province, or even China as a whole, has no official evaluation system in place which can be employed to evaluate the degree of low carbon of urban LCC. This paper aimed to establish a system for evaluating the urban LCC of Guangdong Province based on their actual situations. The system divided the evaluation into six categories, applied the SMART criteria to select suitable indices, and set the reference values for two zones, i.e., the Pearl River Delta zone (PRD) and the non-PRD zone. Monte Carlo and AHP were combined for the calculation of index weights, thus ensuring that the weight judgment matrixes could all pass the consistency check. In addition, this paper also laid down detailed evaluation regulations to improve the operability of this system and guarantee its wide application and better generalization. Finally, one typical urban community was selected to test the operability of this system, and the One-At-a-Time (OAT) method was used to analyze the sensitivity of each index and explore the degree of influence of these indices on evaluation results. The results showed that three indicators corresponding to green building percentage, new energy vehicles percentage and low carbon public transportation had the highest sensitivity. After field test evaluation, the evaluation system was found to have strong operability, and a high accessibility to indicator data. This system cannot only be used as a tool by the Provincial People's Government of Guangdong to evaluate the construction progress and degree of low carbon in LCC, but can also provide a reference point and experience for the Chinese government to draw upon in establishing the evaluation system of LCC at a national level. [ABSTRACT FROM AUTHOR]

An integrated life cycle approach framework, including material flow analysis (MFA), Cumulative Energy Demand (CED), exergy analysis (EXA), Emergy Assessment (EMA), and emissions (EMI) has been constructed and applied to examine the energy efficiency of high speed urban bus transportation systems compared to conventional bus transport in the city of Xiamen, Fujian province, China. This paper explores the consistency of the results achieved by means of several evaluation methods, and explores the sustainability of innovation in urban public transportation systems. The case study dealt with in this paper is a Bus Rapid Transit (BRT) system compared to Normal Bus Transit (NBT). All the analyses have been performed based on a common yearly database of natural resources, material, labor, energy and fuel input flows used in all life cycle phases (resource extraction, processing and manufacturing, use and end of life) of the infrastructure, vehicle and vehicle fuel. Cumulative energy, material and environmental support demands of transport are accounted for. Selected pressure indicators are compared to yield a comprehensive picture of the public transportation system. Results show that Bus Rapid Transit system (BRT) shows much better energy and environmental performance than NBT, as indicated by the set of sustainability indicators calculated by means of our integrated approach. This is because of the higher efficiency of such modality (less affected by traffic, higher vehicle occupancy, suitability for large distance transportation). The study suggests that the higher economic and resource investments performed in order to provide dedicated roads, more modern transport technology and higher speed, translated into a better use of resources and lower environmental pressure, also because of the attraction of an increased number of passengers, who would have otherwise used car transportation modalities. This study also provides a clear evidence that more than one criterion is needed to address a fully reliable and sustainable urban transportation policy. An integrated approach is therefore suggested to support decision making in the presence of complex systems and different kinds of concerns to be taken into proper account. [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]

Providing accessibility by public transportation is one of the main concerns in sustainable transportation development. An appropriate accessibility index should not only take transportation and land use into account but also the people who want to attend activities via the transport system. Travel time as the most common variable is used to indicate the role of transportation. A trip with public transport includes different parts: walking from the origin point to the bus stop or from the bus stop to the destination point, waiting for the bus to arrive, and in-vehicle time. These different parts have different weight values for passengers which affect their tendency towards traveling by public transportation. In this paper, a GIS-based multimodal gravity model is developed based on the weighted door to door travel time to compute accessibility by public transportation. Five main distinct urban activities including employment, education, healthcare, shop, recreation opportunities, and services are considered and the accessibility of the target population at the census block level to these destinations through public transportation is computed. In the next step, in order to consider all activities together and report a single unique index, the five computed accessibility indices are integrated into a composite index using a principal component analysis (PCA). The integrated accessibility measure helps to get an insight into the relative distribution of the benefits of public transportation and its interaction with the land use. The proposed method is applied to the City of Isfahan in Iran. The results indicate where places, for each activity and in overall, would benefit from the better land use and public transportation interaction and where regions would suffer from low accessibility level. This work provides a methodological framework as a tool for measuring the performance of public transportation and its interaction with the land use pattern. • Measuring the accessibility at block polygon level based on weighted door to door travel time, demand, and attraction • Developing a multimodal network to estimate travel time of public transportation including walking, waiting, and in-vehicle • Measure accessibility to five main urban activities and determine the level of accessibility and spatial equity realistically • Applying principal components analysis to composite accessibility to different urban activities into a single unique index. [ABSTRACT FROM AUTHOR]

• Average and marginal emission factors per passenger kilometer traveled are compared. • Marginal energy and emission factors are substantially lower than average factors. • Differences between marginal and average intensity vary by mode and service. • Environmental benefits of public transit are greater considering marginal intensity. • Average factors discount the benefits of shifting travel away from private vehicles. Comparisons of the energy and emission intensity of transportation modes are standard features of sustainable transportation research, policy, and advocacy. These comparisons are typically based on average energy and emission factors per passenger trip or per passenger-kilometer traveled. However, as acknowledged in the energy production sector, comparing average emission factors can misinform policy and other decisions because it fails to represent the marginal impact of changing demand. The objective of this paper is to quantify the difference between average and marginal energy and emission factors for passenger transportation modes. Transportation system operations data are used to estimate energy and emission factors per passenger-kilometer traveled for U.S. urban and intercity travel. Marginal emission factors range from 30% (intercity rail) to 90% (private vehicles) of average factors. For urban travel, private vehicles and public transit have similar average emission factors, but marginal factors are 50% lower for transit. The average emission factor for intercity rail is 10% lower than air travel and 30% lower than private vehicles, but the marginal factor is 60% and 80% lower, respectively. Using average energy and emission factors to represent the impacts of travel by different modes is biased against public transit and discounts the benefits of shifting travel away from private passenger vehicles. [ABSTRACT FROM AUTHOR]

Highlights • Method for precise bus travel time prediction using deep learning. • Models both cross-link (spatial) and cross-temporal correlations. • Designed for urban areas where congestion, events, etc. highly influence flow. • Empirically evaluated on large dataset from the Greater Copenhagen. • Significantly outperforms the compared baseline methods. Abstract Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas. The traditional application of this information, where arrival and departure predictions are displayed on digital boards, is highly visible in the city landscape of most modern metropolises. More recently, the same information has become critical as input for smart-phone trip planners in order to alert passengers about unreachable connections, alternative route choices and prolonged travel times. More sophisticated Intelligent Transport Systems (ITS) include the predictions of connection assurance, i.e. an expert system that will decide to hold services to enable passenger exchange, in case one of the services is delayed up to a certain level. In order to operate such systems, and to ensure the confidence of passengers in the systems, the information provided must be accurate and reliable. Traditional methods have trouble with this as congestion, and thus travel time variability, increases in cities, consequently making travel time predictions in urban areas a non-trivial task. This paper presents a system for bus travel time prediction that leverages the non-static spatio-temporal correlations present in urban bus networks, allowing the discovery of complex patterns not captured by traditional methods. The underlying model is a multi-output, multi-time-step, deep neural network that uses a combination of convolutional and long short-term memory (LSTM) layers. The method is empirically evaluated and compared to other popular approaches for link travel time prediction and currently available services, including the currently deployed model at Movia, the regional public transport authority in Greater Copenhagen. We find that the proposed model significantly outperforms all the other methods we compare with, and is able to detect small irregular peaks in bus travel times very quickly. [ABSTRACT FROM AUTHOR]

Abstract An agent-based modeling for dynamic ridesharing in a multimodal network is proposed in this paper. The study aims to evaluate the performance of dynamic ridesharing system within a multimodal network and explore the competing mechanism between dynamic ridesharing and public transit, with the presence of managed lane facility. The modeling process simulates the interaction between travelers and the network, and applies a heuristic algorithm to model travelers' decision making process under uncertainty. The model is applicable to networks with varying demographics. Multiple scenarios based on the classic Sioux Falls network have been examined. The modeling results demonstrate that the effects of dynamic ridesharing on a network differ with traffic demand and market penetrations of various travel modes. In networks with high travel demand and low market penetration of public transit, the benefits of dynamic ridesharing system on reducing congestion and providing reliable travel time are quite limited. To enhance the effectiveness of dynamic ridesharing, traffic operators may consider project investments on managed lane facilities. In networks with high market penetration of public transit, dynamic ridesharing may attract large amounts of short distance trips and aggravate congestion, especially at the initial launching phase. Policy makers would want to ensure that the existing infrastructure is sufficient to accommodate the extra traffic induced by ridesharing. Ridesharing service providers might also consider proper strategies to avoid "abuse" of the system by short trips and accelerate the market penetration. Highlights • We develop an agent-based model to explore the impact of dynamic ridesharing within a multimodal network. • The competing mechanism between dynamic ridesharing and public transit, with the presence of managed lanes, is quantified. • The benefits of dynamic ridesharing under various travel modes market penetrations are assessed. • The results offer guidance to policy makers and service providers on proper strategies under various network settings. [ABSTRACT FROM AUTHOR]

Abstract Carbon emissions from the Chilean public transport sector have doubled between 2000 and 2013. Thus, the need to find alternative clean-air solutions is becoming increasingly critical. With this in mind, this paper addresses a solution to the aforementioned problem by studying the conversion of both taxis and "colectivos" (public taxis with defined routes) into electric vehicles (EV) to be used in the Chilean public transport sector. In Chile, 80% of taxis are Nissan V16s, meaning that all of these vehicles will be replaced and end up in landfills within the next 5–8 years. This study presents an option to give a second life to these vehicles, addressing environmental and financial issues. It compares emissions from the Nissan V16's conventional internal combustion engine (ICE) with an equivalent electric conversion prototype. Furthermore, it analyses the CO 2 emissions from the EV that has been fully charged by the Chilean grid as well as one that has been charged by a solar photovoltaic system. A solar recharging station for EV taxis is designed, taking advantage of the high levels of solar radiation in Chile. The results show that EV conversion does not lead to real environmental benefits, in comparison to its ICE equivalent, when it is charged with the grid. However, the results also show a considerable decrease in cost and CO 2 emissions per km travelled when using solar energy to charge the batteries. Future challenges include identifying solutions to cope with solar intermittence and minimizing CO 2 emissions during periods of low radiation. Highlights • Conversion of ICE taxis into EVs in Chile. • Comparison of emissions of the Nissan V16 ICE with its electric conversion prototype. • Design of solar charging stations considering solar irradiation available in Chile. • Conversion does not show benefits when EV charged from electricity grid in comparison to ICV. • Results show decrease in running cost and CO2 emissions when EV charged from solar electricity. [ABSTRACT FROM AUTHOR]

Highlights • This is the first application of Pareto-optimization on real data in AHP. • Methodological background of Pareto test is introduced. • Full description of the application procedure is provided. • Conclusions are summarized and suggestions are made to other potential appliers. • Results are directly disposable for public transport development decisions. Abstract Recent findings in operations research and decision theory show that the eigenvectors derived from pairwise comparison matrices are not Pareto optimal in all cases. Thus, looking at the traditional eigenvector method of Saaty, a better approximation may exist for characterizing the decision makers' opinion. The Pareto optimal modified vector may lead to better decisions for better expressing the real intention of the evaluator. This paper aims to examine Pareto-optimality on real data pairwise comparison matrices gained from a public transport Analytic Hierarchy Process (AHP) survey. Moreover, detecting the impact of weight score modification on the whole AHP structure and thus the significance of Pareto test is also in the scope of this study. Further, a detailed description of the general process of Pareto optimal AHP is also included. The application has been conducted in Mersin, Turkey with the purpose of determining public preference on the importance of developing supply quality elements in local bus transportation service. [ABSTRACT FROM AUTHOR]

Highlights • Railway modeling approach for system constraint handling in optimal sizing method. • Handle of operator-imposed criteria: local or sector support, and data assimilation. • Method applied to real railway line of the French railway company in Paris region. • Case study introduced by analyzing real voltage data of two sites over several days. • Energy storage system requirements for future traffic conditions and operator criteria. Abstract In the context of direct-current electrified railway systems, power supply quality issues are a major concern for the railway network operators. Indeed, after signaling systems and track equipment, voltage issues may be the most challenging factor for the capacity of a direct-current railway line. These issues and the high cost of new substations motivate the implementation of wayside energy storage systems to support the system. Since an appropriate sizing methodology is a key step towards an energy storage system implementation in a railway line, in this paper a sizing methodology is presented in detail. As hypothesis, the sizing methodology is defined such that the storage system is able to support the railway line under projected conditions of the rolling stock traffic, along with other technical criteria relevant to the railway operator. A real-time simulation oriented, direct-current railway network modeling approach is proposed and exploited in this sizing methodology. Using this modeling method, the optimal energy storage sizing formulation is described. The objective function to minimize is the trade-off between energy storage capacity and charging power. The proposed sizing method is applied to a real railway line with known power supply quality issues. This case study is introduced by analyzing real voltage measurements on two key sites of the line over several days. Then, the sizing methodology is applied and the results are discussed for the study case, defining the minimal technical requirements to install. The obtained minimal technical requirements for the storage system are considered by the operator to take a cost-effective decision in contrast with the reinforcement of the line with new substations. [ABSTRACT FROM AUTHOR]

Revitalizing the role of public transport systems has the potential benefit of creating sustainable urban mobility and enhancing social inclusion. However, an imbalanced distribution of public transport services across a population may result in adverse effects for some social groups. One of these, the senior population, is seeing rapid growth, giving rise to increasing concerns. In this context, this paper develops a systematic methodology, supported by Geographic Information Systems (GIS), to identify the relative spatial gaps in public transport supply and demand from seniors, such as census tracts (CTs), traffic analysis zones (TAZs), or neighborhoods, considering sustainability at the statistical area (SA) level. A case study of Edmonton, Canada is described to demonstrate the novelty and practical capabilities of this methodology. The presented results indicate that the concentration degree of transport services varies among all CTs and reflects diversity in terms of various transport supply indicators, but the Gini coefficients of the seniors are all found to be relatively small. Distribution of the public transport supply index (PTSI) and the public transport demand index (PTDI) are found to be diverse among all CTs in terms of geographical locations and the number of CTs in different categories. It is also found that Edmonton features some easily distinguishable over-serviced and under-serviced areas, as well some CTs with balanced service—usually in suburban locations far from the city's core. The findings provide policy makers with evidence to validate a quantitative method for evaluating the relative spatial gaps in public transport services for individual CTs in order to promote greater equality of transport opportunities and increase of transport service efficiency among socially disadvantaged groups. [ABSTRACT FROM AUTHOR]

The term 'smart cities' is a widely used, but at the same time a highly fuzzy concept. The fuzziness hinders our understanding on the benefits of its adoption, and explains the existence of many relevant activities with fragmented or distorted views of what a truly smart city is. The aim of this paper is to undertake a comprehensive review of how smart cities are perceived in the literature and in the light of the findings propose a clearer definition. Overview of the key terms, concepts and definitions associated to smart cities, reported in this paper, demonstrates that the definitions found in the academic literature have limited scope, and are overly focused on strategic drivers and specific actions, instead of making explicitly the connection between the concept of smart cities and the creation of environments that promote happiness and wellbeing of their residents-which should be the main function of a city. Following the thorough review on the smart cities literature, the paper proposes a comprehensive, human-centred, and context-free definition for smart cities. This definition brings an endogenous view on smart cities in which the central element is the direct participation of local actors and stakeholders in the process of thinking, defining, planning, and executing social, technological and urban transformations in cities. [ABSTRACT FROM AUTHOR]

Trolleybuses may be an eco-efficient solution for public urban transport. The present paper describes the effects resulting from the modernization of an existing trolleybus transport system in Tychy, Poland. The novel solutions concerning traction design and the use of modern vehicles equipped with traction batteries and generation systems made it possible to achieve the expected positive effects related to environment protection – energy savings and limitation of emission of greenhouse gases – in particular carbon dioxide. Positive experiences have resulted in green light for successive investments – development of a new line almost 5 km long and purchase of new vehicles equipped with traction batteries which make them capable of running the distance of 20 km without traction. The paper provides insights for municipality policy makers responsible for urban transport. It stresses the fact that public transport based on trolleybus networks provides new opportunities for sustainable development of cities. [ABSTRACT FROM AUTHOR]

Tour planning represents a challenging task for individuals visiting unfamiliar tourist destinations, mainly due to the availability of numerous attractions (points of interest, POIs) and the complexity of metropolitan public transit networks. Several web and mobile tourist city guides already support personalized tour recommendations. However, they exclusively consider walking tours; namely, they fail in motivating tourists to use public transportation for reaching far located important POIs, thereby compromising the perceived overall attractiveness of recommended tours. In this paper, we introduce eCOMPASS, a context-aware web/mobile application which derives personalized multimodal tours via selected urban attractions. eCOMPASS is the only available research or commercial tour planner that assists the way arounds of tourists through public transit. Far beyond than just providing navigational aid, eCOMPASS incorporates multimodality (i.e. time dependency) within its routing logic aiming at deriving near-optimal sequencing of POIs along recommended tours so as to best utilize the time available for sightseeing and minimize waiting time at transit stops. Further advancing the state of the art, eCOMPASS allows users to define arbitrary start/end locations (e.g. the current location of a mobile user) rather than choosing among a fixed set of locations. Last, eCOMPASS may assist in scheduling lunch breaks at affordable restaurants, conveniently located along the recommended tours. The provision of the above mentioned unique features of eCOMPASS is based on modeling and solving a complex optimization problem which takes into account a long list of problem variables and constraints. This paper describes the routing algorithm which comprises the core functionality of eCOMPASS. Further, it discusses the implementation details of the web and mobile eCOMPASS applications using the metropolitan areas of Athens (Greece) and Berlin (Germany) as case studies. Evaluation results report positive user attitude as to the tour planning output with respect to attractiveness, meaningfulness and the overall perceived utility. [ABSTRACT FROM AUTHOR]

Nowadays the increasing demand for sustainable mobility has fostered the introduction of innovative propulsion systems also in the public transport sector in order to achieve a significant reduction of pollutant emissions in highly congested urban areas. This paper describes both the design and the optimization of an environmentally friendly hybrid bus (hereafter referred to as “HYBUS”) for urban public transportation. After a preliminary description of the main features of the hybrid architecture, this paper assessed, through numerical simulations, the fuel economy potential of the hybridization in real world driving conditions. The promising results of this first part of the study led to the development of a first prototype. The first prototype of the bus was built by integrating an innovative hybrid propulsion system featuring a plug-in series architecture into the chassis of an old IVECO 490 TURBOCITY. The bus is 12 m long and capable to host up to 116 passengers in the original layout. The project relied on a modular approach where the powertrain could be easily customized for size and power, depending on the specific application. The prototype was then extensively tested in the city of Genoa, Italy, an urban context extremely challenging for a hybrid powertrain due to its frequent uphill routes and significant road grades. The outcomes of the test campaign confirmed the simulations forecasts, and fostered additional analysis aimed to optimize the energy management strategy of the hybrid powertrain. Numerical simulations were then used in order to identify more refined energy management strategies capable of further enhancing the fuel economy potential of the hybrid architecture. Consequently, a novel energy management was developed, and virtually tested, to manage the HYBUS in a more effective way. The results demonstrated the interesting potential of such hybrid architecture. [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]

In many cities of the world, bicycle infrastructure projects are implemented to foster more sustainable transportation systems. However, such projects have often raised questions regarding their public funding, as they entail considerable costs. This paper reviews cost–benefit analysis (CBA) frameworks as these are presently used to assess bicycle infrastructure projects. Specific focus is on the CBA framework developed in Copenhagen, Denmark, a self-declared “city of cyclists”. In this framework, costs and benefits of car and bicycle, the two major urban transport modes, have been assessed and are compared across accidents, climate change, health, and travel time. The analysis reveals that each km travelled by car or bike incurs a cost to society, though the cost of car driving is more than six times higher (Euro 0.50/km) than cycling (Euro 0.08/km). Moreover, while the cost of car driving is likely to increase in the future, the cost of cycling appears to be declining. The paper concludes with a discussion of the applicability of the Copenhagen CBA framework to advance sustainable transport planning and to motivate and justify urban restructuring. [ABSTRACT FROM AUTHOR]

The need to consider environmental and social issues on a par with economic concerns in policy, plan, programme and project making in order to achieve sustainable development has been widely recognized. However, to date there has been no adequate implementation, and economic issues are frequently prevailing over other aspects. In many developing and emerging economies, a perceived urgency to promote economic growth frequently means that environmental and social costs of development are overlooked. It is within this context that this paper explores the consideration of environmental and social issues in transport policy, plan and programme (PPP) making in Brazil, an emerging economy which over recent years has invested heavily in its transport infrastructure. Aiming at establishing the extent to which environmental and social issues are considered and identifying the barriers for better practice, legal and institutional frameworks for transport PPP making, the substantive focus of PPPs as well as perceptions of actors involved in their preparation are investigated. Results show that whilst the need to respect environmental and social issues is recognized in sectoral guidelines and underlying values, in practice they are poorly considered. Main constraints to a better consideration of sustainable development in transport PPP making include the lack of instruments for a systematic consideration, the nature of existing PPP making processes and the dominance of political and economic interests. Moreover, suggestions for improvement are limited and mainly relate to external pressures. In this context, Strategic Environmental Assessment in particular would need to be introduced as an instrument for a more rigorous and clearly prescribed consideration of environmental and social issues. [ABSTRACT FROM AUTHOR]

The hydrogen-driven fuel-cell electric buses, which bear the advantages of emission free, high storage content and long mileage range, provide a promising choice for decarbonizing the public transportation system. Also, the operation centers for fuel-cell electric buses can be upgraded as the hydrogen-based microgrids to achieve higher economic benefits and resilience levels. To support the low-carbon and resilience-oriented planning of microgrid-aided bus centers, this paper presents a two-stage robust optimization formulation with integer corrective decisions. In particular, the first-stage problem is to optimize the microgrid siting decisions as well as the capacity configuration of renewable-powered electrolyzers, fuel-cell stacks and hydrogen refueling stations for maximizing the annualized benefits. Then, a min – max problem is introduced in the second-stage to improve the load survival value subject to the planning decisions and the worst-case contingency scenario. Also, various corrective actions, e.g., the emergency response of fuel-cell electric buses using vehicle-to-grid functionality, the energy scheduling of stationary equipment in hydrogen refueling stations and the adaptive network reconfiguration, are considered for resilience enhancement of the local power network. As the resulting robust optimization formulation is with a challenging mixed-integer recourse structure, the nested column-and-constraint generation algorithm is designed. Numerical results on exemplary power network and public transportation system validate the effectiveness of the proposed planning methodology, which has the potential to be profitable under normal operations while improving the load survivability under severe disastrous occasions. Moreover, our customized nested column-and-constraint generation shows a satisfactory solution performance, which facilitates prompt disaster relief actions of energy-transportation network. • Hydrogen-based microgrids are planned to support the fuel-cell bus operation centers. • Robust optimization model is proposed for low-carbon and resilience-oriented planning. • The emergency response of fuel-cell buses for back-up power supply is modeled. • Efficient solution is obtained by nested column-and-constraint generation algorithm. • Methods are verified on exemplary power network and public transportation system. [ABSTRACT FROM AUTHOR]

The development of 5G technology provides huge potential for the Internet of Vehicles (IoV). In the IoV scenario, the secure exchange of traffic warning data is necessary to guarantee the safety of pedestrians, vehicles, and public transport facilities. However, there remain a series of challenging issues to achieve data secure sharing while ensuring efficiency. Current solutions such as the data outsourcing storage of traditional centralized cloud servers may cause information leakage due to their incomplete credibility. To address these problems mentioned above, this paper proposes an edge-aided searchable data sharing scheme based on blockchain for IoV in a 5G environment. First, the proposed scheme stores the search index of the keyword and the hash value of the data ciphertext on the blockchain to ensure its integrity and authenticity, and the smart contract automatically verifies the search trapdoor. And then, an edge caching mechanism is designed to quickly feedback on users' retrieval needs based on a cloud-edge collaboration model. In addition, attribute-based searchable encryption is used to ensure that only the users who meet the access attributes and policy can decrypt the encrypted traffic warning data, which can achieve fine-grained access control and keyword search simultaneously. Finally, security analysis and performance evaluation demonstrate that our scheme is secure and more efficient compared with other schemes. [ABSTRACT FROM AUTHOR]

Eco-driving is one of the many options to reduce fuel consumption and emissions from transit operations. However, it is not yet clear how effective eco-driving is for different transit service and fuel types. As policymakers consider implementing eco-driving, they also need comparisons of eco-driving against other fuel-conserving strategies, such as purchasing alternative fuel vehicles. Using a case study of transit operations in Atlanta, Georgia, United States, this paper evaluated eco-driving for two very different service types – local urban service and express service. The authors simulated the implementation of transit eco-driving strategies using an innovative, streamlined algorithm designed to minimize fuel consumption by limiting instantaneous vehicle specific power while maintaining average speed and conserving total distance. Fuel consumption and fuel-cycle emissions were compared across the monitored driving cycles and their modified eco-driving cycles. The savings from eco-driving were also compared to fuel and emissions reductions expected via the conversion of the transit fleets to compressed natural gas (CNG), another popular fuel conservation strategy. The results showed that eco-driving would be a potentially very cost-effective strategy for local and express bus transit operations. [ABSTRACT FROM AUTHOR]

Well-designed timetables, with appropriate coordination between the last feeder trains and the last connecting trains so that passengers can enjoy “smooth” transfers, are desired by passengers and are a major service goal of metro corporations. This paper addresses the strategically last train scheduling problem in urban railway transit networks. First, two practical optimization models for last trains are proposed to minimize the standard deviation of transfer redundant times and to balance the last train transfers in subway networks. Second, we design a new heuristic algorithm to solve the developed models. Finally, the models and the heuristic are applied to the Beijing subway network. The results show that the last train timetables for the Beijing subway network have been improved by increasing the average travelling speed and the average technical speed by 9.9% and 8.42%, respectively. This means that the last train passengers can save 97.4 min in total in the Beijing subway network when riding last trains. [ABSTRACT FROM AUTHOR]

Estimating the future state of air quality associated with transport policies and infrastructure investments is key to the development of meaningful transportation and planning decisions. This paper describes the design of an integrated transportation and air quality modelling framework capable of simulating traffic emissions and air pollution at a refined spatio-temporal scale. For this purpose, emissions of Nitrogen Oxides (NO x ) were estimated in the Greater Montreal Region at the level of individual trips and vehicles. In turn, hourly Nitrogen Dioxide (NO 2 ) concentrations were simulated across different seasons and validated against observations. Our validation results reveal a reasonable performance of the modelling chain. The modelling system was used to evaluate the impact of an extensive regional transit improvement strategy revealing reductions in NO 2 concentrations across the territory by about 3.6% compared to the base case in addition to a decrease in the frequency and severity of NO 2 hot spots. This is associated with a reduction in total NO x emissions of 1.9% compared to the base case; some roads experienced reductions by more than half. Finally, a methodology for assessing individuals’ daily exposure is developed (by tracking activity locations and trajectories) and we observed a reduction of 20.8% in daily exposures compared to the base case. The large difference between reductions in the mean NO 2 concentration across the study domain and the mean NO 2 exposure across the sample population results from the fact that NO 2 concentrations dropped largely in the areas which attract the most individuals. This exercise illustrates that evaluating the air quality impacts of transportation scenarios by solely quantifying reductions in air pollution concentrations across the study domain would lead to an underestimation of the potential health gains. [ABSTRACT FROM AUTHOR]

Nowadays, passengers in urban public transport systems do not only seek a short-time travel, but they also ask for optimizing other criteria such as cost and effort. Therefore, an efficient routing system should incorporate a multiobjective analysis into its search process. Several algorithms have been proposed to optimally compute the set of nondominated journeys while going from one place to another such as the generalization of the algorithm of Dijkstra. However, such approaches become less performant or even inapplicable when the size of the network becomes very large or when the number of criteria considered is very important. Therefore, we propose in this paper an advanced heuristic approach whereby a Genetic Algorithm (GA) is combined with a Variable Neighborhood Search (VNS) to solve the Multicriteria Shortest Path Problem (MSPP) in multimodal networks. As transportation modes, we focus on railway, bus, tram and pedestrian. As optimization criteria, we consider travel time, monetary cost, number of transfers and the total walking time. The proposed approach is compared with the exact algorithm of Dijkstra, as well as, with a standard GA and a pure VNS. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that the proposed combination GA–VNS represents the best approach in terms of computational time and solutions quality for a real world routing system. [ABSTRACT FROM AUTHOR]

We put forward a new data object called the public transit travel time cube and demonstrate how the cube can be used in the analysis of transit travel time changes over space and time. The travel time cube contains the shortest path transit travel time between sets of origins and destinations in the city, at all times of day. Once computed, a wide range of investigations become readily available to the transit planner or transportation researcher. We conduct three demonstrative analyses using travel time cubes for the Wasatch Front, Utah and the Portland region in Oregon. Our studies investigate how travel times were impacted by service cuts and expansions in the two regions respectively and the impact this had on jobs accessibility. We also use the travel time cube to study the last mile problem, and compute the travel time savings and the stability gained by solving the last mile problem with bicycling. The paper concludes with an expanded discussion on the merits of the travel time cube and outlines four avenues for continued research. [ABSTRACT FROM AUTHOR]