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Space Syntax theory is a methodological framework for describing and analysing the relationships between spaces and buildings. The fundamental building blocks of Space Syntax are called axial lines, which represent lines of sight from a specific point in space. These lines can then be used to quantitatively analyse the integration (or attractiveness) of an area. However, many public squares and transport hubs are crossed by roads which prove a hinderance when walking although not impairing the sightlines of pedestrians. The aim of this paper is to test how this limitation affects the concept and whether Space Syntax is still useful to understand how people move in such areas. Seven public squares and transport hubs in Malmö (Sweden) were selected and their Space Syntax integration was calculated. These values were compared with pedestrian detections from the same areas based on video recordings from a drone. When aggregating the areas into several levels based on their integration value, the result shows a clear link between Space Syntax integration and the number of pedestrian detections made at each location, demonstrating how this measure could be useful for understanding micromobility usage on a small scale. [ABSTRACT FROM AUTHOR]

Autonomous solutions for transportation are emerging worldwide, and one of the sectors that will benefit the most from these solutions is the public transport by shifting toward the new paradigm of Mobility as a Service (MaaS). Densely populated areas cannot afford an increase in individual transportation due to space limitation, congestion, and pollution. Working towards more effective and inclusive mobility in public areas, this paper compares user experiences of autonomous public transport across Baltic countries, with the final goal of gaining an increased insight into public needs. User experience was evaluated through questionnaires gathered along pilot projects implementing a public transportation line, using an automated electric minibus between 2018 and 2019. To have sufficient diversity in the data, the pilot projects were implemented in several cities in the Baltic Sea Area. The data analysed in this paper specifically refer to the cities of Helsinki (Finland), Tallinn (Estonia), Kongsberg (Norway), and Gdańsk (Poland). Across all cities, passengers provided remarkably positive feedback regarding personal security and safety onboard. The overall feedback, which was very positive in general, showed statistically significant differences across the groups of cities (Kongsberg, Helsinki, Tallinn and Gdansk), partially explicable by the differences in the route design. In addition, across all cities and feedback topics, males gave a lower score compared to females. The overall rating suggests that there is a demand for future last-mile automated services that could be integrated with the MaaS concept, although demand changes according to socio-economic and location-based conditions across different countries. [ABSTRACT FROM AUTHOR]

In this paper we apply a novel JAVA version of a model on the homeostasis of human red blood cells (RBCs) to investigate the changes RBCs experience during single capillary transits. In the companion paper we apply a model extension to investigate the changes in RBC homeostasis over the approximately 200000 capillary transits during the ~120 days lifespan of the cells. These are topics inaccessible to direct experimentation but rendered mature for a computational modelling approach by the large body of recent and early experimental results which robustly constrain the range of parameter values and model outcomes, offering a unique opportunity for an in depth study of the mechanisms involved. Capillary transit times vary between 0.5 and 1.5s during which the red blood cells squeeze and deform in the capillary stream transiently opening stress-gated PIEZO1 channels allowing ion gradient dissipation and creating minuscule quantal changes in RBC ion contents and volume. Widely accepted views, based on the effects of experimental shear stress on human RBCs, suggested that quantal changes generated during capillary transits add up over time to develop the documented changes in RBC density and composition during their long circulatory lifespan, the quantal hypothesis. Applying the new red cell model (RCM) we investigated here the changes in homeostatic variables that may be expected during single capillary transits resulting from transient PIEZO1 channel activation. The predicted quantal volume changes were infinitesimal in magnitude, biphasic in nature, and essentially irreversible within inter-transit periods. A sub-second transient PIEZO1 activation triggered a sharp swelling peak followed by a much slower recovery period towards lower-than-baseline volumes. The peak response was caused by net CaCl2 and fluid gain via PIEZO1 channels driven by the steep electrochemical inward Ca2+ gradient. The ensuing dehydration followed a complex time-course with sequential, but partially overlapping contributions by KCl loss via Ca2+-activated Gardos channels, restorative Ca2+ extrusion by the plasma membrane calcium pump, and chloride efflux by the Jacobs-Steward mechanism. The change in relative cell volume predicted for single capillary transits was around 10−5, an infinitesimal volume change incompatible with a functional role in capillary flow. The biphasic response predicted by the RCM appears to conform to the quantal hypothesis, but whether its cumulative effects could account for the documented changes in density during RBC senescence required an investigation of the effects of myriad transits over the full four months circulatory lifespan of the cells, the subject of the next paper. Author summary: Each human red blood cell traverses narrow capillaries about 1000 to 2000 times per day. Cell deformability is essential for smooth transits and this requires tight control of cell volume well below spherical maxima. The pump-leak ion-flux balance controlling red cell volume involves sodium-potassium and calcium pumps and a constellation of passive membrane transporters. PIEZO1, a mechanosensitive ion channel plays a central role, activating transiently during capillary transits. The main question, not accessible to direct experimentation, is what happens to red cell volume and composition in vivo during each capillary transit. We attempted answering this question by applying novel extensions to a tried and tested model of red cell homeostasis that encodes the current knowledge on the subject. The results revealed an unexpected up-down biphasic volume response characterized by a sharp initial surge caused by CaCl2 influx via PIEZO1, associated with osmotic fluid gain, followed by a slow reversal, a prediction in conflict with prevailing views. The infinitesimal amplitude of the predicted volume changes precludes them playing a significant role in flow dynamics. In the following paper we explore how the biphasic response helps explain the age-related changes red cells experience during their long circulatory lifespan. [ABSTRACT FROM AUTHOR]

High‐dependability Internet of Vehicle (IoV) is essential for urban transit, since it contributes to the safe operation of vehicles, and even to the safety of the entire city. This paper introduces the IoV cluster model with network slicing. Based on this model, an integrated vehicles clustering and network slicing scheme for IoV networks is introduced to guarantee the dependability of different traffics in IoV. The network slicing scheme is proposed to divide the wireless frequency resource of the IoV network into separate logical networks. A ChristmasTree$Christmas \ Tree$ analysis model, based on age of information, is utilized as an integrated indicator for evaluating the dependability of the IoV system in urban transit. Asynchronous reinforcement learning with advantage actor‐critic is employed to optimize the integrated dependability of IoV. It is demonstrated in simulations that the proposed IoV cluster incorporating the network slicing scheme can guarantee the integrated dependability of the IoV system. Moreover, by the application of reinforcement learning, the system dependability can be further improved. [ABSTRACT FROM AUTHOR]

Today's roadways are subject to traffic congestion, the deterioration of surface-assets (often due to the overreliance on private vehicle traffic), increasing vehicle-operation and fuel costs, and pollutant emissions. In Abu Dhabi, private car traffic forms the major share on urban highways, as the infrastructure was built to a high quality and the public transport network needs expansion, resulting in traffic congestion on major highways. These issues are arguably addressable by appropriate decisions at the planning stage. Microsimulation modeling of driving behavior in Abu Dhabi is presented for empirical assessment of traffic management scenarios. This paper presents a technique for developing, calibrating, validating, and the scenario analysis of a detailed VISSIM-based microsimulation model of a 3.5 km section of a 5-lane divided highway in Abu Dhabi. Traffic-count data collected from two sources, i.e., the local transport department (year 2007) and municipality (2007 and 2015–2016) were used. Gaps in traffic-counts between ramps and the highway mainline were noted, which is a common occurrence in real-world data situations. A composite dataset for a representative week in 2015 was constructed, and the model was calibrated and validated with a 15% (<100 vehicles per hour) margin of error. Scenario analysis of a potential public bus transport service operating at 15 min headway and 40% capacity was assessed against the base case, for a 2015–2020 projected period. The results showed a significant capacity enhancement and improvement in the traffic flow. A reduction in the variation between vehicle travel times was observed for the bus-based scenario, as less bottlenecking and congestion were noted for automobiles in the mainline segments. The developed model could be used for further scenario analyses, to find optimized traffic management strategies over the highway's lifecycle, whereas it could also be used for similar evaluations of other major roads in Abu Dhabi post-calibration. [ABSTRACT FROM AUTHOR]

Autonomous rail rapid transit (ART) is a new type of multiunit, articulated, rubber-wheeled urban transport system. The noise sources of ART have significant time-varying characteristics. It is unsuitable to track the error signal by installing too many error sensors, which poses a significant challenge in the active noise control of ART. Thus, this paper proposes a convolutional fuzzy neural network-based active noise cancellation approach without error sensors to solve this problem. The proposed approach utilizes convolutional neural network (CNN) to extract the noise signal characteristics of ART and trains multiple noise source signals using a CNN to estimate the virtual error signal in the target area. In addition, the proposed approach adopts fuzzy neural network (FNN) for adaptive adjustment of filter weight coefficients to achieve real-time noise tracking control with fast convergence and small error in the convergence process. The experimental results demonstrate that the proposed approach can effectively reduce ART low-frequency noise without error sensors, and the average sound pressure level in the target area is reduced more compared with conventional approaches. [ABSTRACT FROM AUTHOR]

Ferry shipping is an indispensable method of public transportation, especially in areas with well-developed river systems or coastal areas. The increasing demand for transport requires additional visits and introduces the problem of ship visit schedule engineering at ferry terminals with stochastic servicing time. In this paper, we propose a ferry visit planning problem to maximize the total profit, in which the berthing time, berthing location, and servicing time for each ferry visit are optimized. Then, a mixed-integer nonlinear programming model is proposed to formulate the focal problem. We propose a tailored solution method to convert the mixed-integer nonlinear programming model to a mixed-integer linear programming model. We further devise an inserting algorithm to test the performance of our model. A comparison between the results of the basic instance yielded by our model and those of the inserting algorithm validates our model and solution method. We then conduct sensitivity analyses of the impacts of different numbers of existing ferry visits and added ferry visits, different expectations of the real time taken by all the ferry visits, and different distribution patterns of existing ferry visits, to further validate the performance of our model. [ABSTRACT FROM AUTHOR]

Rural areas have been marginalized when it comes to flexible, quality transportation research. This review article brings together papers that discuss, analyze, model, or experiment with demand-responsive transportation systems applied to rural settlements and interurban transportation, discussing their general feasibility as well as the most successful configurations. For that, demand-responsive transportation is characterized and the techniques used for modeling and optimization are described. Then, a classification of the relevant publications is presented, splitting the contributions into analytical and experimental works. The results of the classification lead to a discussion that states open issues within the topic: replacement of public transportation with demandresponsive solutions, disconnection between theoretical and experimental works, user-centered design and its impact on adoption rate, and a lack of innovation regarding artificial intelligence implementation on the proposed systems. [ABSTRACT FROM AUTHOR]

In the last decade, in Europe and the US, carsharing has become a mainstream transportation mode offering a sustainable solution to serious urban problems such as pollution, economic crisis, congestion, and parking. In Greece, carsharing is currently entering its commercial phase. Planners and providers strive to gain an insight into the factors influencing the use of carsharing to effectively implement carsharing systems (CSS). In this context, understanding the motives and usage conditions are considered necessary. Based on a qualitative analysis (semi-constructed interviews, n = 52), this paper identifies motivational patterns as well as personal characteristics of potential users that can be further explored through quantitative research methods. During the data analysis process, participants' responses were classified into categories that revealed not only the factors that motivated them but also unveiled the challenges they face when utilizing carsharing schemes. These factors were the following: familiarity, comfort, mindset, everyday life, usability, and economy. Next, these factors were analyzed further based on the personal characteristics of the respondents preparing the ground for quantitative research in future research initiatives. Notably, the present findings could be beneficial to operators, policymakers, and stakeholders endeavoring to appraise shared mobility schemes in Greece and Mediterranean countries in general. [ABSTRACT FROM AUTHOR]

The spatial accessibility of urban parks is an important indicator of the livability level of cities. In this paper, we propose a comprehensive multimodal two-step floating catchment area (CM2SFCA) method which integrates supply capacity, the selection probability of individuals, and variable catchment sizes into the traditional multimodel 2SFCA method. This method is used to measure park accessibility in Wuhan, China. The results show that the spatial distribution of park accessibility under the proposed method is variant. High accessibility areas are clustered near the Third Ring Road with strong supply capacity parks, and low accessibility areas are distributed in the western and southern regions. Compared with the single-model accessibility (bicycling, driving, and public transit) method, we found that the multimodal spatial accessibility, combining the characteristics of three single transportations, can provide a more realistic evaluation. We also explore the spatial relationship between park accessibility and population density by bivariate local Moran's I statistic and find that the Low Ai-High Pi area is located in the center of the study area, and the Low Ai-Low Pi area is located at the edge of the study area, with a relatively discrete distribution of parks and weak supply capacity. These findings may provide some insights for urban planners to formulate effective policies and strategies to ease the spatial inequity of urban parks. [ABSTRACT FROM AUTHOR]

The median path problem (min-sum criterion) is a common problem in graph theory and tree networks. This problem is open to study because its applications are growing and extending in different fields, such as providing insight for decision-makers when selecting the optimal location for non-emergency services, including railroad lines, highways, pipelines, and transit routes. Also, the min-sum criterion can deal with several networks in different applications. The location problem has traditionally been concerned with the optimal location of a single-point facility at either a vertex or along an edge in a network. Recently, numerous investigators have investigated this classic problem and have studied the location of many facilities, such as paths, trees, and cycles. The concept of the median, which measures the centrality of a vertex in a graph, is extended to the paths in a graph. In this paper, we consider the problem of locating path-shaped facilities on a tree network. A new modified and improved algorithm for finding a median single path facility of a specified length in a tree network is proposed. The median criterion for optimality considers the sum of the distances from all vertices of the tree to the path facility. This problem under the median criterion is called the ℓ-core problem. The distance between any two vertices in the tree is equal to the length of the unique path connecting them. This location problem usually has applications in distributed database systems, pipelines, the design of public transportation routes, and communication networks. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) will soon become the primary means of transportation for millions of people. However, the introduction of these vehicles may lead to increased traffic and changes in people's travel patterns. To address this issue, one solution is to promote the use of AVs in a shared and public manner. However, the success of this approach depends on public acceptance of public AVs. If the promoters of public AVs are unaware of how people perceive this technology, it is possible that these vehicles will face failure in the market. Therefore, this paper aims to identify the factors that influence the willingness to use public AVs. To achieve this, an autonomous public van acceptance model (APVAM) was developed by adapting the unified theory of acceptance and use of technology (UTAUT2) through structural equations modeling. In this study, 824 citizens of Tehran participated in a field study and completed an online questionnaire. The research results indicated that the variable of effort expectancy indirectly affects the use of autonomous public vans (APVs), while the variables of performance expectancy, facilitating condition, hedonic motivation, and perceived PUnTrust directly affect their use. The research also found that various factors such as gender, level of education, individualism/collectivism, travel purpose, the dominant travel mode, marital status, occupation, age, household wealth, number of vehicles owned, and the price of the current vehicle of the household have a moderating effect on the APVAM. Furthermore, it was revealed that individuals who primarily use personal or public transport for their daily trips are more influenced by the perceived enjoyment of APVs in increasing their willingness to use these vehicles, compared to those whose predominant mode of travel is walking or cycling. [ABSTRACT FROM AUTHOR]

Whilst urban areas are thriving in trialling new mobility services (NMS), rural environments, often perceived as areas of low demand for travel, struggle to attract investments for creating more mobility solutions alongside traditional public transport (PT) services, making residents more reliant on private cars. This paper describes how policy interventions for introducing NMS in rural areas should be guided by big data to capture real and accurate travel behaviours, therefore avoiding perceived biases and potentially underestimating demand. In the UK, the provision of transport in rural areas is solely linked to population density and does not consider differences between places and residents' travel habits. The proposed data‐driven decision‐making process used trip‐chains from mobile network data (MND) to derive recent and accurate travel patterns from residents and provide the right mix of on‐demand mobility services alongside existing fixed scheduled public transport (PT). The manuscript describes the steps carried out to study three rural areas at low, medium and high population density in the UK: a data landscape to select study areas; the development of an activity‐based model, which uses anonymised mobile network data (MND) aggregated at trip‐chains level to derive travel patterns; and the development of an on‐line questionnaire and focus groups with rural communities to co‐designing solutions based on attitudes towards NMS. Results demonstrated that a data‐driven decision making process to introduce NMS is a viable solution for updating demand for travel in rural areas, offering a broad understanding of mobility needs and the relationship of interdependency with nearby areas, therefore allowing policy makers to create users‐centric transport solutions. The study concludes by drawing recommendations for NMS for passengers and goods for the NMS proposed for a rural areas [Demand Responsive Transport (DRT), Micro‐mobility and delivery drones]. [ABSTRACT FROM AUTHOR]

Current electric vehicle solutions offer the possibility of a fully electrified bus fleet, although due to financial constraints, most cities cannot afford it. Therefore, the possibility of battery replacement is a needed alternative to the electrification process of a city's bus fleet. The aim of this study is to investigate the needs of cities and present the concept of battery replacement in an electric bus. The research was based on two groups of selected Polish cities: (1) up to 150,000 inhabitants, and (2) up to 1 million inhabitants. The research part includes an analysis of the means of transport in provincial cities in Poland, an analysis of the kilometers covered by the city fleet, the average distances covered by buses per day, and an estimate of the number of battery replacements. The concept is based on current technological solutions. The description of the concept includes the proposed battery and the technology used, the placement of the battery in the vehicle, and the replacement scheme. Research indicates that the concept can be used with existing technology but will be more justifiable for a larger city due to the higher fleet load. The paper shows the importance of researching bus electrification solutions and that modern solutions can improve existing urban networks in cities. [ABSTRACT FROM AUTHOR]

Considering the proposed strict new constraints of public authorities, decarbonization has become a key trend in recent years. Although several countries have started the process of decarbonization through the introduction of electric vehicles in their public services, for many countries, especially developing countries, transportation is still a hard sector to decarbonize. The presence of obsolete and polluting vehicles discourages citizens from using public transport and thus incentivizes the use of private vehicles, which create traffic congestion and increase emissions. Based on these considerations, this paper aimed to implement a simulation for a public service in Eritrea, evaluating whether it is possible to take a long trip using an electric minibus. A case study is implemented highlighting the barriers of electrifying transportation in this area, producing results on fuel consumption and service reliability. In the case study, four scenarios are presented to estimate the service. The scenarios evaluate the possibility to perform from three to five recharges. Fewer charges mean longer charging time, leading to a 2 h charging phase in Scenario 1, while recharging more than twice along the route will lead to shorter 30 min charges, as in Scenario 3. The case study also highlights the relevance of the slope in electric vehicle performance, as reported for the case of Asmara–Massawa travel ( E c o n s = 6.688 kWh). Finally, an environmentally sustainable solution, such as a 92 kWh/day photovoltaic plant, is proposed to power the service. [ABSTRACT FROM AUTHOR]

An escalator is an essential large-scale public transport equipment; once it fails, this inevitably affects the operation of the escalator and even leads to safety concerns, or perhaps accidents. As an important structural part of the escalator, the foundation of the main engine can cause the operation of the escalator to become abnormal when its fixing bolts become loose. Aiming to reduce the difficulty of extracting the fault features of the footing bolt when it loosens, a fault feature extraction method is proposed in this paper based on empirical wavelet transform (EWT) and the gray-gradient co-occurrence matrix (GGCM). Firstly, the Teager energy operator and multi-scale peak determination are used to improve the spectral partitioning ability of EWT, and the improved EWT is used to decompose the original foundation vibration signal into a series of empirical mode functions (EMFs). Then, the gray-gradient co-occurrence matrix of each EMF is constructed, and six texture features of the gray-gradient co-occurrence matrix are calculated as the fault feature vectors of this EMF. Finally, the fault features of all EMFs are fused, and the degree of the loosening of the escalator foundation bolt is identified using the fused multi-scale feature vector and BiLSTM. The experimental results show that the proposed method based on EWT and GGCM feature extraction can diagnose the loosening degree of foundation bolts more effectively and has a certain engineering application value. [ABSTRACT FROM AUTHOR]

Transport is one of the essential services contributing to a country's development. The global pandemic caused by Covid-19 has affected different sectors, one of the most affected being public transport services. In Spain there are several transport companies, some of them are public, and others are private. The main objective of this paper is to study the profitability, liquidity, and solvency situation of transport companies considering their size and type of management. The impact of the Covid-19 pandemic on transport companies will be analyzed too. After descriptive analysis of the companies by ratios, the Altman Z-score indicator has been used. This methodology has been used to assess whether the health crisis has compromised the solvency of these companies. The results have shown that privately managed companies are healthier and have a lower risk of insolvency than public companies. In addition, small companies were more solvent than big companies. This study is an essential contribution to the academic literature, public administration, and management of companies. [ABSTRACT FROM AUTHOR]

This paper presents a prototype of a disinfection system for public transport specifically aiming to disinfect surfaces contaminated with the SARS-CoV-2 virus on buses using 222 nm wavelength far-ultraviolet light (far-UVC). Our study involved testing the developed technical system installed in a 12 m long M3 category urban bus, an investigation of optimal far-UVC light angles, and the determination of disinfection parameters for bus seat disinfection. The study identified the ideal positioning of a light source for effective disinfection and analyzed three disinfection scenarios, considering zone coverage, disinfection time, and energy demand. A subsystem employing real-time occupancy monitoring enhances the disinfection process in crowded areas of buses. An energy efficiency assessment model is proposed for optimizing energy consumption. Furthermore, the energy consumption analyses in different disinfection scenarios provide valuable insights for optimizing energy usage in public transport disinfection. [ABSTRACT FROM AUTHOR]

In the context of the network operation of urban rail transit systems, disruptions caused by signal interruptions influence not only the operation of the service at a single station but also the level of service of the whole network. Moreover, it is even possible to induce the cascading failure of the urban rail transit network. Therefore, it is essential to maintain the real-time dynamic monitoring of abnormal stations in urban rail transit systems for security reasons. Based on the large amounts of automated fare collection (AFC) data, a real-time calculation method to estimate the influence intensity of the passenger flow is presented, the spatiotemporal distribution of the influence characteristics is analyzed, and the propagation law of disruptions in the urban rail transit network is explored. First, the fluctuation threshold of passenger flow in a normal situation for all stations was calculated. Accordingly, abnormal stations influenced by the disruption were identified. Then, an evaluation method for calculating the influence intensity of the passenger flow was proposed. Finally, a real-world case study based on the Beijing rail transit system was conducted. All abnormal stations were identified dynamically and displayed in real time, and the distribution and propagation law of abnormal stations were constructed by spatiotemporal diagrams. The influence intensity of passenger flow was analyzed in detail from the perspective of the whole network and representative stations. The results revealed that transfer stations were more vulnerable to the effects of disruption, and the duration for which these stations were affected was longer than that of ordinary stations. Moreover, short-distance travelers were less affected by the disruption than long-distance travelers. The method proposed in this paper can provide a theoretical basis for rail management departments to grasp the characteristics of passenger flow in real time, formulate disposal measures dynamically, and provide more accurate information services for passengers. [ABSTRACT FROM AUTHOR]

This paper studies the distributionally robust fair transit resource allocation model (DrFRAM) under the Wasserstein ambiguity set to optimize the public transit resource allocation during a pandemic. We show that the proposed DrFRAM is highly nonconvex and nonlinear, and it is NP-hard in general. Fortunately, we show that DrFRAM can be reformulated as a mixed integer linear programming (MILP) by leveraging the equivalent representation of distributionally robust optimization and monotonicity properties, binarizing integer variables, and linearizing nonconvex terms. To improve the proposed MILP formulation, we derive stronger ones and develop valid inequalities by exploiting the model structures. Additionally, we develop scenario decomposition methods using different MILP formulations to solve the scenario subproblems and introduce a simple yet effective no one left-based approximation algorithm with a provable approximation guarantee to solve the model to near optimality. Finally, we numerically demonstrate the effectiveness of the proposed approaches and apply them to real-world data provided by the Blacksburg Transit. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Division of Computing and Communication Foundations [Grant 2153607] and the Division of Civil, Mechanical and Manufacturing Innovation [Grant 2046426]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1159. [ABSTRACT FROM AUTHOR]

Amid the impact of COVID-19, the public's willingness to travel has changed, which has had a fundamental impact on the ridership of urban public transport. Usually, travel willingness is mainly analyzed by questionnaire survey, but it needs to reflect the accurate psychological perception of the public entirely. Based on Weibo text data, this paper used natural language processing technology to quantify the public's willingness to travel in the post-COVID-19 era. First, web crawler technology was used to collect microblog text data, which will discuss COVID-19 and travel at the same time. Then, based on the Naive Bayes classification algorithm, travel sentiment analysis was carried out on the data, and the relationship between public travel willingness and urban public transport ridership was analyzed by Spearman correlation analysis. Finally, the LDA topic model was used to conduct content topic research on microblog text data during and after COVID-19. The results showed that the mean values of compelling travel emotion were -0.8197 and -0.0640 during and after COVID-19, respectively. The willingness of the public to travel directly affects the ridership of urban public transport. Compared with the COVID-19 period, the public's fear of travel infection in the post-COVID-19 era has significantly improved, but it still exists. The public pays more attention to the level of COVID-19 prevention and control and the length of travel time on public transport. [ABSTRACT FROM AUTHOR]

There are many sorts of day-to-day choices that are such that, if enough people were to choose one way rather than another, serious harm could be avoided or reduced, and yet it does not seem that any one such choice will itself make a difference. Consider, for example, how our collective consumer choices have various serious environmental and social consequences, and yet for many products, it is doubtful that one purchase more or less will itself make a difference to these outcomes. How are we to understand what each of us ought to do in these sorts of contexts? This paper further advances and illuminates a thesis that I have argued for elsewhere: that a purely 'non-instrumental' approach to this question is not satisfactory. A necessary and central part of understanding how to think about an individual choice in these contexts is showing that it does matter for instrumental reasons - for reasons having to do with its ability to have an influence on the outcome. Once a core instrumental solution is found, other moral considerations can build on top. I argue for this by way of an examination of a new non-instrumental approach advanced by Wieland and van Oeveren: a participation-based approach. I also identify what I think is the main source of resistance to my thesis: a mistaken conflation of two different problems. [ABSTRACT FROM AUTHOR]

Publishing transport data on the Web for consumption by others poses several challenges for data publishers. In addition to planned schedules, access to live schedule updates (e.g. delays or cancellations) and historical data is fundamental to enable reliable applications and to support machine learning use cases. However publishing such dynamic data further increases the computational burden for data publishers, resulting in often unavailable historical data and live schedule updates for most public transport networks. In this paper we apply and extend the current Linked Connections approach for static data to also support cost-efficient live and historical public transport data publishing on the Web. Our contributions include (i) a reference specification and system architecture to support cost-efficient publishing of dynamic public transport schedules and historical data; (ii) empirical evaluations on route planning query performance based on data fragmentation size, publishing costs and a comparison with a traditional route planning engine such as OpenTripPlanner; (iii) an analysis of potential correlations of query performance with particular public transport network characteristics such as size, average degree, density, clustering coefficient and average connection duration. Results confirm that fragmentation size influences route planning query performance and converges on an optimal fragment size per network. Size (stops), density and connection duration also show correlation with route planning query performance. Our approach proves to be more cost-efficient and in some cases outperforms OpenTripPlanner when supporting the earliest arrival time route planning use case. Moreover, the cost of publishing live and historical schedules remains in the same order of magnitude for server-side resources compared to publishing planned schedules only. Yet, further optimizations are needed for larger networks (>1000 stops) to be useful in practice. Additional dataset fragmentation strategies (e.g. geospatial) may be studied for designing more scalable and performant Web apis that adapt to particular use cases, not only limited to the public transport domain. [ABSTRACT FROM AUTHOR]

In this paper, we describe a comparative analysis of a bus route scheduling problem as part of timetable trips. We consider the current uptake of electric buses as a viable public transportation option that will eventually phase out the diesel-engine-based buses. We note that, with the increasing number of electric buses, the complexity related to the scheduling also increases, especially stemming from the charging requirement and the dedicated infrastructure behind it. The aim of our comparative study is to highlight the brevity with which a multi-agent-system-based scheduling method can be helpful as compared to the classical mixed-integer linear-programming-based approach. The multi-agent approach we design is centralized with asymmetric communication between the master agent, the bus agent, and the depot agent, which makes it possible to solve the multi-depot scheduling problem in almost real time as opposed to the classical optimizer, which sees a multi-depot problem as a combinatorial heuristic NP-hard problem, which, for large system cases, can be computationally inefficient to solve. We test the efficacy of the multi-agent algorithm and also compare the same with the MILP objective designed in harmony with the multi-agent system. We test the comparisons first on a small network and then extend the scheduling application to real data extracted from the public transport of the Maltese Islands. [ABSTRACT FROM AUTHOR]

Recent advances in vehicle technology offer new opportunities for an electric, automated, modular bus (MB) unit with an adjustable capacity to be applied to transit systems, promising to tackle the resource allocation challenges of traditional buses in coping with uneven travel demand. Drawing on the concept of modular vehicles, this paper introduces a novel scheduling system in which MB units can be combined/separated from fulfilling imbalanced trip demands through capacity adjustments. We develop an optimization model for determining the optimal formation and trip sequence of MB units. In particular, given that the vehicles are electrically powered, battery range limits and charging plans are considered in the system scheduling process. A column-generation-based heuristic algorithm is designed to efficiently solve this model, with constraints related to travel demand and charging station capacity incorporated into the master problem and the trip sequence for modular units with limited energy solved by the subproblem. Taking real data from transit operations for numerical examples, the proposed model performs well in terms of both algorithmic performance and practical applications. The generated optimal MB dispatching scheme can significantly reduce the operating cost from $1534.31 to $1144.26, a decrease of approximately 25% compared to conventional electric buses. The sensitivity analysis on the MB dispatch cost and battery capacity provides some insights for both the scenario configuration and the battery selection for MB system implementation. [ABSTRACT FROM AUTHOR]

Looking ahead: transforming conventional public transport prioritization into C-ITS G5 services. The city of Frankfurt aims to digitize its public transport prioritization system in order to fulfill the requirements of future public transport communication standards and, moreover, to build on this very infrastructure for the development of imminent C-ITS services. Therefore, the communication systems of the mobility and transport provider VGF (Verkehrsgesellschaft Frankfurt am Main mbH) are being revised fundamentally by implementing new technologies for Car2X C-ITS G5 communication. The hardware components of the C-ITS system are strategically positioned with the help of a newly developed planning tool that identifies and determines the range of communication. For highly significant sites and locations of the hardware components, the calculated data are validated by utilizing measurements within a mobile setup. The operational stability and the development of previously unused potential are then carried out via the combination of the C-ITS services TSP (Traffic Signal Priority) and GLOSA (Green Light Optimized Speed Advisory). The overlay of the C-ITS services results in a high level of operational stability. As a result, potentials can be adequately employed through the sensible shifting of waiting times to the stops and a smooth flow of traffic through information on optimal speed and remaining times of the traffic light potentials. This paper presents a new methodology with which it is now possible to plan and evaluate C-ITS with regard to service distribution and radio propagation. [ABSTRACT FROM AUTHOR]

With the increasing scale of the urban subway, the total energy consumption of the subway has increased dramatically and poses a great challenge to the comfort of passengers and the punctuality of train operation. In order to ensure on-time train operation and passenger comfort, and at the same time reduce the energy consumption of subway operation, this paper proposes a Proximal Policy Optimization (PPO)-based optimization algorithm for the optimal control of subway train operation. Firstly, a reinforcement learning architecture for optimal control of subway train operation is constructed with the position and speed of train operation as the reinforcement learning state, energy consumption and comfort as the optimization objectives, and train operation time as the constraint. The proposed reinforcement learning model is trained by the PPO algorithm, and the reward scaling is added to the training process to accelerate the training speed and improve the efficiency of the algorithm. The experimental results show that the proposed PPO with reward scaling algorithm can effectively reduce train energy consumption and improve passenger comfort while ensuring on-time train operation. [ABSTRACT FROM AUTHOR]

Introduction of the research problem. Urbanization drives Digital City Projects (DCPs) to create smarter urban environments using advanced technologies. DCPs aim to make cities more connected and responsive, adapting to changing needs. The objective of this paper is to evaluate the performance of DCPs in megalopolises, focusing on Kharkiv and Dnipro in Ukraine. The previous works done. The various literature sources demonstrate the rise of Digital Cities stemming from Smart Cities. Kharkiv and Dnipro in Ukraine exemplify digitalization's role amid Russian aggression. Exposition of the main research material. The performance of the theoretical urbogeosystemic approach and its UOM in the provision of practical Digital City projects. This subsection delves into the practical application of the urbogeosystemic approach and its Urban Ontological Model (UOM) in DCPs. The UOM guides urban studies by defining components and relationships. Implementing DCPs begins with building simulation models using LiDAR data. Case Study First - Kharkiv: A feasible perspective of a full-format DCP implementation. This subsection discusses implementing a DCP in Kharkiv, emphasizing data integration from OpenStreetMap (OSM) and LiDAR. The authors propose that a DCP should serve as a comprehensive model of a real city, encompassing all its structural elements and key objects, going beyond the capabilities of a typical GIS project. Possible user’s scenarios include energy consumption analysis, population estimation, and visibility gradients assessment. The subsection highlights the comprehensive DCP approach with LiDAR data processing software (iQ City CCM) and urban geosituational analysis. Case Study Second - Kharkiv: a perspective of geomarketing within the “Digital Kharkiv” project as a routine GIS one. This subsection delves into the integration of geomarketing into the "Digital Kharkiv" project. Geomarketing plays a pivotal role in mapping socioeconomic elements tied to market interactions. "Digital Kharkiv," primarily sourced from OSM data, is lauded for its versatility in urban studies during peacetime and war. The text urges exploration of geomarketing within "Digital Kharkiv" in the context of postRussian aggression rehabilitation, particularly in optimizing humanitarian object placements. Changes in geomarketing potential preand post-invasion in various city districts have been analyzed, highlighting areas with stagnation and those witnessing growth due to population resettlement. Case Study Third - Dnipro: implementation of a typical GIS-project for analyzing provision of the city population with public transportation infrastructural networks. This subsection discusses the implementation of the "Digital Dnipro" project as part of the DCP framework. The project focuses on analyzing the provision of public transportation networks in the city of Dnipro. It utilizes data from OSM to create a virtual model of the city, which includes attribute information for urban objects. This subsection also highlights the impact of war on urban planning and the need for sustainable updates to adapt to changing conditions. Conclusion. This section summarizes the key findings and takeaways from the research on DCPs in Ukrainian cities like Kharkiv and Dnipro. It highlights the importance of an urbogeosystemic approach in implementing DCPs effectively. The study emphasizes the flexibility and efficiency of the relevant GIS tools in urban research and transformation. [ABSTRACT FROM AUTHOR]

The aim of the paper is to provide an empirical framework of the ageing process in Italy, with a focus on aging in place and mobility behaviour of the elderly, as emerging from two national surveys: the “Aspects of Daily Life” survey by ISTAT and the ISFORT mobility survey. Results show that the Italian cities and towns are sufficiently age-friendly, with some improvement opportunities to be implemented. Loneliness and isolation represent a warning sign, hindering the aging in place. Finally, the study confirms that the Italian older adults use public transport only a few times, in favour of private cars. [ABSTRACT FROM AUTHOR]

Nowadays, with the growing population, demand for efficient public transportation systems has increased. Magnetic levitation (maglev) trains are one of the best choices for the future. There are three separate systems in a classical magnetic train to achieve desirable performance. Hence, several control systems and sensors are essential for train operation. Accordingly, the classical maglev trains include complex structures and they are so expensive. This paper presents the design, optimization, and control of a combined magnetic train structure with the integrated performance of suspension and guidance and a complementary propulsion system. These combined topologies provide a simple design, enure a more convenient movement, and reduce construction and operation costs. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos de Geografia: Revista Colombiana de Geografía is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Since December 2019, COVID-19 began to spread throughout the world for nearly two years. During the epidemic, the travel intensity of most urban residents has dropped significantly, and they can only complete inflexible travel such as "home to designated hospital" and "home to supermarket" and some special commuting trips. While ensuring basic travel of residents under major public health emergency, there is also a problem of high risk of infection caused by exposure of the population to the public transport network. For the discipline of urban transport, how to use planning methods to promote public health and reduce the potential spread of diseases has become a common problem faced by the government, academia and industry. Method: Based on the mobility perspective of travel agents, the spatial analysis methods such as topological model of bus network structure, centrality model of public transport network and nuclear density analysis are used to obtain the exposure risk and spatial distribution characteristics of public transport from two aspects of bus stops and epidemic sites. Results: The overall spatial exposure risk of Wuhan city presents an obvious "multi center circle" structure at the level of bus stops. The high and relatively high risk stops are mainly transport hubs, shopping malls and other sites, accounting for 35.63%. The medium and low-risk stops are mainly the villages and communities outside the core areas of each administrative region, accounting for 64.37%. On the other hand, at the scale of epidemic sites, the coverage covers 4018 bus stops in Wuhan, accounting for 36.5% of all bus stops, and 169 bus lines, accounting for 39.9% of all routes. High risk epidemic sites are mainly concentrated in the core areas within the jurisdiction of Wuhan City, and in the direction of urban outer circle diffusion, they are mainly distributed in the low and medium risk epidemic sites. According to the difference of the risk level of public transport exposure, the hierarchical public transport control measures are formulated. Discussion: This paper proposes differentiated prevention and control countermeasures according to the difference of risk levels, and provides theoretical basis and decision-making reference for urban traffic management departments in emergency management and formulation of prevention and control countermeasures. [ABSTRACT FROM AUTHOR]

Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accessibility plays a crucial role in evaluating and optimizing the service quality of public transport systems. Traditionally, bus accessibility research studies mainly focus on the evaluation from the perspective of the aggregate model and analyze the service radius of the site considering the front end of travel. This paper aims to model microlevel bus accessibility calculation and accurate bottleneck station identification according to the opportunity method with AVL (Automatic Vehicle Location) data. The research featured the following control elements: (1) redefining bus accessibility with bus station as the research object; (2) running time between interstation sections is selected as the control index; (3) A new measure index of accessibility. The farthest direct reachable distance is proposed. Moreover, by introducing the quality control algorithm, the study establishes a model of upper and lower limits of accessibility based on the probability index of bilateral norms, which can be employed to identify the bottleneck stations according to the significance level. Harbin City is selected as the case study for methodology verification and shows that the accessibility method is effective and reliable. Additionally, by taking the travel demand in the region as the adjustment parameter of the quality control algorithm, the study demonstrates the ideal accessibility interval of stations and accurately locates all bottlenecks. The study presented here adds to our understanding of accessibility in the construction and management of buses and provides an essential reference for the following optimization. [ABSTRACT FROM AUTHOR]

With the increase of the world's population, the means of transportation and vehicles that adapt to the times are still difficult to cope with the increase in traffic volume. The traffic problem can be said to be a worldwide problem. However, with the development of artificial intelligence, the emergence of smart transportation has brought new development to modern transportation, and the application of smart transportation and big data is inseparable. In contemporary society, the widespread use of the Internet allows the public to fully exercise their rights to participate in social management and conduct public opinion supervision, which provides a great impetus for the development of online public opinion. However, due to the huge scale of information, some false and harmful information and opinions will inevitably be mixed into it, which will make the network public opinion unable to perform its due function smoothly. Therefore, it is necessary to carry out highly effective management activities on the network public opinion. This paper studies the multidimensional dynamics and prediction model of network public opinion based on the integration of smart transportation and big data; the aim is to design a simple and effective forecasting model to provide traffic management departments with good public opinion forecasting and analysis methods so as to make better decisions. This paper analyzes the related technologies of smart transportation and network public opinion and designs a prediction model of smart transportation network public opinion. Finally, this paper uses rough set theory to optimize the model and compares the data before and after optimization. The results are as follows: the data correlation coefficient before and after optimization is 0.988, and the two-tailed significance level is 0.471, which proves that the results before and after processing are highly correlated, and the two sets of data have no significant difference, proving that the optimization of the model is effective, simplifies the analysis process, and does not change the results. [ABSTRACT FROM AUTHOR]

The harmonious development of urban rail transit, underground space engineering, and social economy is the key to regional sustainable development. Based on synergetic theory, this paper constructs the coupling coordination evaluation system of "rail transport-social economy" composite system at the scale of a city cluster. With this system, the coupling and coordinated development pattern and characteristics of "rail transit-social economy" in the Yangtze River Delta city cluster from 2002 to 2020 were analyzed. The paper makes a horizontal comparison with the Beijing-Tianjin-Hebei city cluster and the Pearl River Delta city cluster, as well as analyzes the differences in development and existing problems, and puts forward policy suggestions for rail and urban underground space development. The results show that: (1) The rail transit of 11 cities in the Yangtze River Delta shows a "step by step" development pattern. That is "national central city, provincial city, second-tier city, third-tier city, etc.", accompanied by periodic changes of coupling and coordination degree. In addition, there is also the phenomenon of unbalanced development within the region; (2) From 2002 to 2020, the development of rail transit in the three city clusters shows a situation of "the overall supply is lacking and lags behind the social economy for a long time". Among them, the Pearl River Delta city cluster has the most serious lags. Multi-channel financing, speeding up the construction of the rail transit scale according to local conditions, and improving operating efficiency are considered to be the keys to solve this problem; (3) In general, the coupling between rail transit and the social economy in the Yangtze River Delta city cluster is better than that in the Beijing-Tianjin-Hebei city cluster and the Pearl River Delta city cluster, but the coordination is at a slight disadvantage. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous improvement of urban public transportation capacity, citizens' travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression (MLR), K-Nearest Neighbor (KNN), eXtreme Gradient Boosting (XGBoost), and Gated Recurrent Unit (GRU) as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the "Huitian Area" of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation. [ABSTRACT FROM AUTHOR]

The feeder bus, as an important component of urban public transportation systems, is capable of filling the existing service gap left by urban rail transit to satisfy feeder demand. Two challenges to taking advantage of the feeder bus are identified. The first challenge is to identify the service gap, and the second one is to strengthen the coordination of multilevel urban traffic modes. Given these two challenges, this paper established a route design model that introduced a feeder bus demand coefficient to quantify feeder demand for each bus stop. To assist the design of feeder bus routes, an optimization approach based on the tabu search algorithm was proposed. Considering the sizeable combinational optimization problem, the algorithm decomposed this problem into three phases: feeder area partition, candidate stop selection, and route design problem. In the first phase, the entire area was partitioned into several subareas and the specific boundary nodes were moved into an adjacent subarea. Then, the stops with the minimum overall travel cost from every subarea were chosen as candidate ones under the limit of operating costs. The last step was to find the shortest circular route to visit each candidate stop and determine the operating direction. The proposed optimization had been implemented in a downtown area of Suzhou, China, in order to verify its effectiveness through the performance in terms of the quality of optimal solutions and computing efficiency. In addition, a possibility of simplifying a multiple circular route design problem to a single route design problem utilizing spatial clustering is provided to enhance the computing efficiency in a large-scale network. [ABSTRACT FROM AUTHOR]

The rapid aggregation of modern urban population and the rapid growth of car travel lead to traffic congestion, environmental pollution, and other problems. In view of the limited land resources in our country, it is impractical to meet residents' travel demand by blindly increasing traffic supply. Therefore, addressing the urban road congestion problem for sustainable development of modern cities, the paper makes research on residents' travel behavior characteristics and travel preference under the condition of multimodal transportation to formulate reasonable traffic demand management strategy for the guide on public traffic demand, bus priority strategy, and congestion management. The operation characteristic of each transportation mode is analyzed by comparing its related traffic and economic characteristics. Multimode traffic choice behavior is discussed by establishing multiple logistic regression models to analyze the main influencing factors to travelers' social and economic attributes, travel characteristics, and preference based on travel survey data of urban residents. The paper proposes the development of an urban public transportation system and travelling mode shift from cars to public transportation as reasonable travel structure for congestion management and sustainable development of modern cities. [ABSTRACT FROM AUTHOR]

Modern urban and metropolitan areas face numerous problems associated with transport activity, such as pollution, congestion, noise or traffic incidents. One of the solutions to cope with these issues may be encouraging people to use means of public transport instead of private cars. However, that requires the fulfillment of demands that present, as well as potential, passengers have. One of the most important passengers' demands is safety of all operations of public transport. In case of tram transport one aspect of safety is associated with the process of boarding the tram and alighting from it at stops separated from tram tracks by roadway, which forces passengers to enter the road. It is hazardous for all passengers and poses an additional risk for disabled passengers. In this paper an analysis of interactions between tram passengers and drivers of road vehicles at such tram stops, during the boarding and alighting from the tram was performed. For that purpose also a classification of tram stops stations was conducted, with the number of traffic lanes that passengers have to pass through being the main criterion. Also, the review of literature on this subject was conducted. For the research during the pilot study the tram network in Metropolis GZM in southern Poland was chosen and all tram stops stations on the network were inspected in order to obtain characteristics necessary for classification. Research during this pilot study allowed to identify conflict interactions that take place at tram stops. Examples of such interactions have been presented in the paper. Paper also contains conclusions and propositions of changes that may be implemented at tram stops that may lead to increase of the level of safety of passengers who try to board the tram or alight from the tram. [ABSTRACT FROM AUTHOR]

The paper presents the results of research in tourism as one of the most important factors in the public passenger transport demand. The research has been carried out for the requirements of creating a Tourism Satellite Account (TSA) for the Republic of Croatia for 2016. Traffic and tourism, as strategic economic activities of the Republic of Croatia, have the characteristics of complex and dynamic systems, mutually conditioned by the guidelines of the demand and supply chain of tourist product values. Therefore, the paper is oriented towards the analysis of the significance of tourism for public road passenger transport in the Republic of Croatia. During 2016, road intercity passenger transport carried 50.4 million passengers, out of which 98 percent in domestic and 2 percent in international transport. Passenger road transportation is marked by mild seasonal oscillations, with the summer period marked by the lowest level of demand, and the period from January to March by the largest number of passengers. The provision of public road transportation services in 2016 realised a revenue of 1.8 billion kuna for 433 business subjects registered in this activity. The trends indicate an increase in the number of travelled kilometres by buses with passengers. [ABSTRACT FROM AUTHOR]

The competitiveness of a travel chain largely depends on the travel conditions along the sequence of journeys within the chain. This paper shows a method to analyse and to optimize the service quality along a travel chain. Travel comfort is a very important qualitative feature of the public transportation service, where travel comfort is used in a broader sense to describe ride quality and transfer quality including mobility, information, safety, security, and naturally comfort aspects. The analysis of travel comfort in the literature regards public transportation services. Several synthetic indices, which consider user judgment about service aspects, were developed to describe travel comfort, and comprehensive analyses have been published. However, to describe the competitiveness of the public transport the focus from the individual services should be moved toward the integrated service of the travel chain from the beginning to the end. The characteristics of travel comfort along the travel chain should be described and the location and rate of necessary interventions should be identified. In this paper we analyse the travel comfort features of travel chains. This paper proposes a method, which describes the travel comfort characteristics with synthetic indices based on the individual comfort indices of travel components, and uses a fuzzy approach to give an overall analysis of comfort conditions along the travel chain. The proposed method helps to identify the quality fluctuation and the weak points of a travel chain and makes the attractiveness of alternative travel chains comparable. An illustrative case study was carried out for one of the major transportation corridor of Budapest (Hungary), to exemplify the approach, where the validity of the method was tested as well. The results confirmed the usefulness and applicability of the methodology; by its application very valuable insights can be gained regarding the location and type of the necessary interventions. The results of our research are helpful to evaluate the actual service level of sustainable alternatives of individual car usage and to promote modal shift towards sustainable transportation modes. [ABSTRACT FROM AUTHOR]

Taxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related influential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation between taxi demand and socio-economic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City. The geographically weighted regression (GWR) model is used to describe the influence factors of spatial heterogeneity of the taxi demand and visualize the spatial distributions of parameter estimations. Results indicate that during the peak hours, there are some differences in taxi demand between workdays and weekends. Residential density and housing prices increase the number of taxi trips. Road density, parking lot density and bus station density are positively associated with the taxi demand. It is also found that the higher of the proportion of commercial area and public service area, the greater of the taxi demand, while the proportion of residential area and the land use mix have a negative impact on taxi demand. This paper provides some references for understanding the internal urban environmental factors generating from the taxi travel demand, and provides insights for reducing the taxi vacancy rate, forecasting taxi temporal-spatial demand and urban public transportation system planning. [ABSTRACT FROM AUTHOR]

Linear induction motors (LIMs) have been extensively adopted in urban railway transportation given their nonadhesion thrust and simple stator structure. However, these LIMs undergo unfavorable power factor and efficiency considering their copper and eddy losses. Recently, linear flux-switching permanent-magnet motors (LFSPMs) have attracted the attention of researchers due to their high power density, simple structure, easy heat dissipation, high efficiency, and high power factor. However, so far, no quantitative comparison exists between LFSPMs and LIMs of the size 1:1 to prove the feasibility of LFSPMs to be used in railway transit systems. Therefore, a comparative analysis between the two motors is conducted in this paper. First, this paper investigates the LIMs for railway transit using the finite element method (FEM). Second, the LFSPMs for railway transit is designed and optimized. Third, the electromagnetic performance of the two motors is compared and analyzed through the FEM. Finally, a small-sized prototype of the LFSPMs is constructed to validate the FEM results. It is concluded that LFSPMs offer a favorable thrust force, have high efficiency, and high power factor but have large normal force and force ripple. Therefore, applying LFSPM requires a reliable supporting device and a shock absorber. [ABSTRACT FROM AUTHOR]

Smart card data is increasingly used to investigate passenger behavior and the demand characteristics of public transport. The destination estimation of public transport is one of the major concerns for the implementation of smart card data. In recent years, numerous studies concerning destination estimation have been carried out--most automatic fare collection (AFC) systems only record boarding information but not passenger alighting information. This study provides a comprehensive review of the practice of using smart card data for destination estimation. The results show that the land use factor is not discussed in more than three quarters of papers and sensitivity analysis is not applied in two thirds of papers. In addition, the results are not validated in half the relevant studies. In the future, more research should be done to improve the current model, such as considering additional factors or making sensitivity analysis of parameters as well as validating the results with multi-source data and new methods. [ABSTRACT FROM AUTHOR]

Human contact is a key issue in social interactions for autonomous systems since robots are increasingly appearing everywhere, which has led to a higher risk of conflict. Particularly in the real world, collisions between humans and machines may result in catastrophic accidents or damaged goods. In this paper, a novel stop strategy related to autonomous systems is proposed. This control method can eliminate the vibrations produced by a system's movement by analysing the poles and zeros in the model of autonomous vehicles and goods. Using the pole placement technique, the motion of a system is guaranteed to be more stable, more flexible and smoother. Moreover, several control profiles are employed in the switching mechanism to choose the proper vibration-free effect. The main contributions of this paper are (i) the recommendation of an active stopping planner using different smooth generators from a modelling study, (ii) the validation of their physical characteristics and (iii) the launching of a switching algorithm based on the socially aware navigation framework of a robot. This theoretical work is based on the virtual environment of MATLAB, and the experiment is implemented in the practical platform of an automated guided vehicle. From these results, it can be seen that the proposed approach is robust, effective and feasible for applications in storehouse management, public transportation or factory manufacturing. [ABSTRACT FROM AUTHOR]

In most big cities, public transports are enclosed and crowded spaces. Therefore, they are considered as one of the most important triggers of COVID-19 spread. Most of the existing research related to the mobility of people and COVID-19 spread is focused on investigating highly frequented paths by analyzing data collected from mobile devices, which mainly refer to geo-positioning records. In contrast, this paper tackles the problem by studying mass mobility. The relations between daily mobility on public transport (subway or metro) in three big cities and mortality due to COVID-19 are investigated. Data collected for these purposes come from official sources, such as the web pages of the cities' local governments. To provide a systematic framework, we applied the IBM Foundational Methodology for Data Science to the epidemiological domain of this paper. Our analysis consists of moving averages with a moving window equal to seven days so as to avoid bias due to weekly tendencies. Among the main findings of this work are: a) New York City and Madrid show similar distribution on studied variables, which resemble a Gauss bell, in contrast to Mexico City, and b) Non-pharmaceutical interventions don't bring immediate results, and reductions to the number of deaths due to COVID are observed after a certain number of days. This paper yields partial evidence for assessing the effectiveness of public policies in mitigating the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In order to evaluate the URTPSQ (Urban Rail Transit Passenger Service Quality) comprehensively, find the shortage of URTPSQ, find out the difference between the actual service situation and the passenger's expectation and demand, and provide passengers with better travel services, a passenger-oriented KANO-Entropy-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method is proposed and applied in this paper. Firstly, a KANO model is applied to select the service quality indicators from the 24 URTPSQ evaluation sub-indicators, according to the selection results, the KANO service quality indicators of URTPSQ are constructed. Then the sensitivity of the KANO service quality indicators based on the KANO model are calculated and ranked, the PS (Passenger Satisfaction) of each KANO service quality indicator by using the Entropy-TOPSIS method is calculated and ranked. Based on the difference between the sensitivity degree rank and the satisfaction degree rank of each KANO service quality indicator, determine the service quality KANO indicators of the URTPSQ that need to be improved significantly. A case study is conducted by taking the Chengdu subway system in China as a background. The results show that the Chengdu subway operation enterprises should pay attention to the must-be demand first, then the one-dimensional demand, finally the attractive demand. The three indicators, including transfer on the same floor in the station, service quality of staffs of urban rail transit enterprises, and cleanness in the station and passenger coach, need to be improved urgently. For the managers and operators of urban rail transit system, the passengers' must-be demand should be satisfied first if the KANO model is applied to evaluate the service. The indicators with highest sensitivity degree and lowest TOPSIS value should be improved based on the KANO-Entropy-TOPSIS model. [ABSTRACT FROM AUTHOR]

Commuters are the stable travel group for the public transportation (PT) service system. Accurately identifying the PT commuters is conducive to promoting PT service quality and development of urban sustainable transportation. This paper extracts individual PT travel chain information and constructs individual travel knowledge graphs of PT passengers based on the association matching algorithm and the theory of multilayer planning. A mixed dataset is formed by associating individual travel chains with travel survey data. Seven travel characteristic indicators regarding travel performance and spatiotemporal travel characteristics are extracted. The identification model of PT commuters is developed based on a three-layer backpropagation neural network (BPNN). The optimal model structure of neuron node number, transfer function, and learning rate are discussed quantitatively according to the minimization of model errors. The evaluation indexes of overall accuracy and kappa coefficient of the constructed model are 94.5% and 87.9% separately. The results indicate that the model identification accuracy is acceptable, and the proposed characteristic indicators and systematic modelling procedure are effective. Then, the model performance is compared with the other five machine learning models further. The results confirm that the proposed model has a better identification accuracy and viability, and the model performance will improve with the increase of the sample size. [ABSTRACT FROM AUTHOR]