Public Transport Research Articles

Shared Mobility's Role in Sustainable Mobility: Past, Present, and Future

This article reviews shared mobility, a prominent urban transportation concept with considerable potential to contribute to more sustainable urban mobility. Shared passenger mobility spans diverse services, often leveraging technological advances and disruptions such as smartphones and data analytics to optimize transport resources. Given the broad range of services, a shared mobility taxonomy is proposed, accommodating evolving services. Key challenges for delivering efficient and effective shared mobility services with lower environmental impacts are also identified. Finally, the International Transport Forum transport demand models are used to analyze policy implications and potential effects quantitatively. This article presents a possible scenario for the global evolution of these services to 2050. Results emphasize shared mobility's role in transport decarbonization in the present and future and show that shared mobility may reduce resource use and mobility externalities (e.g., CO2, local pollutants, congestion, urban space use) but that the uptake will differ between Global South and Global North cities.

Air Route Design of Multi-Rotor UAVs for Urban Air Mobility

UAVs will present significant air traffic in the urban airspace in the future, which brings new challenges to urban air traffic management and control. This paper presents an air route design scheme for multi-rotor UAVs in urban airspace to enable UAV operations at orderly levels. The air routes include legs and intersections, which are the three-dimensional channels of UAV flight. Based on the concept of structured and layered urban airspace, the cylindrical pipeline flight leg is designed, and the operation concept, characteristic parameters and flight procedures of along-road and roundabout intersections are proposed. By defining UAV conflict risk and intersection service level metrics, the operation situation of UAVs is quantitatively evaluated. Taking an urban transportation scenario as a case, the proposed route design scheme is simulated in different scale UAV operating scenarios. The results show that the number of UAVs at the intersection is positively correlated with the conflict probability, the number of crossing routes is negatively correlated with the intersection passing rate, and the UAV arrival rate is positively correlated with the intersection average passing time. The along-road type intersection is suitable for the area with fewer crossing routes and sparse UAVs, while the roundabout type intersection is adapted for the area with more crossing routes and dense UAVs. This research provides a new idea for urban UAV air route design, which is helpful in promoting the standardized management of UAVs and accelerating the integration of UAVs into urban airspace.

The Current State and Future of the Urban Cold Chain: A Review of Algorithms for Environmental Optimization

Cold chains are essential in providing people with food and medicine across the globe. As the global environmental crisis poses an existential threat to humanity and societies strive for more sustainable ways of life, these critically important systems need to adapt to the needs of a new era. As it is, the transportation sector as a whole accounts for a fifth of global emissions, with the cold chain being embedded in this old fossil-fuel-dependent infrastructure. With the EU is passing regulations and legislation to cut down on emissions and phase out polluting technologies like combustion engine vehicles, the next couple of decades in Europe will be defined by rapid infrastructural change. For logistics and cold transportation, this shift presents many opportunities but also highlights the need for innovation and new research. In this literature review, we identify pressing issues with the current urban cold chain, review the recent research around environmental optimization in urban logistics, and give a cross-section of the field: what the trending research topics in urban logistics optimization across the globe are, and what kind of blind spots are identifiable in the body of research, as well as changes arising with future green logistics infrastructure. We approach the issues discussed specifically from the point of view of refrigerated urban transportation, though many issues extend beyond it to transportation infrastructure at large.

Airport Express Passenger Flow Prediction Based on “Time-Feature” Cooperative Attention

Accurate prediction of airport express passenger flow is the basis for realizing intelligent, refined and efficient management and control of the airport rail transit system, and is of great significance to improving airport service levels and operational efficiency. Due to the numerous influencing factors that overlap with each other, and the complex mechanism of factors affecting passenger flow timing, accurate prediction of airport express passenger flow is extremely challenging. This paper proposes an airport express rail passenger flow prediction model based on the "time-feature" collaborative attention mechanism, which achieves accurate capture of the impact of multi-dimensional factors on airport express rail passenger flow in different time series. Experiments were conducted based on actual passenger flow data of the Beijing Capital International Airport Express Rail Link, and the results showed the effectiveness of the proposed method.

Land Use, Travel Patterns and Gender in Barcelona: A Sequence Analysis Approach

Transport systems are essential for the path toward sustainable urbanisation and the transition to more sustainable living. Recently, European cities have undergone substantial changes, and suburbanisation is posing new challenges. Suburban areas are often more affordable in terms of housing, but these neighbourhoods tend to be car-oriented. This leads to higher commuter costs, immobility, transport and time poverty, pollution, higher accident rates and a lack of social interactions. To offer sustainable mobility options to citizens, we must comprehensively understand, together with their individual characteristics, their specific mobility practices and the built environment where they live. This study is centred on the Barcelona Metropolitan Region, which has a public transport network that covers its entire area. The aim of this study is to examine the relationships between travel behaviour, transport mode use, individual characteristics and built environment characteristics in the place of residence using detailed information sources. Herein, we used data from the 2018 to 2021 annual travel survey conducted in the Barcelona region, together with land use and sociodemographic information. Our findings suggest that transport policies have encouraged sustainable mobility practices, particularly in the centre of Barcelona. Despite the positive results, considerable disparities exist between the inner and outer city, with a notable decline in sustainable mobility practices in the latter, due to the uneven distribution of basic services and uneven provision of public transport, together with lower density areas. Our results demonstrate that this uneven distribution reduces the available sequence profiles of inhabitants. In conclusion, the promotion of sustainable mobility policies necessitates further advances in transport, city and land-use planning that consider equity, gender, the socioeconomic profiles of citizens and mixed urban planning.

Integrated and sustainable performance evaluation of urban rail transit systems using fuzzy sustainability index

The complexity of prominent dimensions in urban rail transportation sector arises from the involvement of several stakeholders and the various interacting variables for evaluating transportation performance. This study uses a fuzzy logic-based method to present an index-based framework for sustainable performance assessment. The proposed framework presents 4 main indicators which include environmental, economic, financial and social sustainability, 14 sub-indicators, 4 main indicators for transportation system effectiveness with 13 sub-indicators and 1 main indicator for multi modal integration with 6 sub-indicators. The performance ratings and importance weights of the transport sustainability attributes are evaluated using a fuzzy logic approach. A numerical illustration is provided to demonstrate the applicability of the approach. The results show that system capacity (passenger capacity of a route), comfort and customer service (passenger environment and ride quality, maintenance cycle) and multimodal integration (park and ride facilities) were found to be weaker attributes. The proposed methodology facilitates the systematic understanding of the prominent attributes for achieving integrated and sustainable transport performance by using the ‘Fuzzy Transport Sustainability Index’.

Service Quality and Behavioral Intention Analysis of Passengers on Small Electric Public Transportation: A Case Study of Electric Tuktuk in the Philippines

Service Quality and Behavioral Intention Analysis of Passengers on Small Electric Public Transportation: A Case Study of Electric Tuktuk in the Philippines

Perceptions of surgery in Nicaragua: A cross-sectional survey study within the surgery for the people project.

Barriers to medical care include lack of proper infrastructure and equipment; however, cultural barriers to care and poorly perceived quality of care, especially surgical care, can also negatively impact a patient's utilization of healthcare services. This study used patient-survey data from three unique municipalities in Nicaragua to examine pre-hospital barriers to care, including previous experience with healthcare, and how those experiences impact patient perceptions of surgery and care-seeking behavior. Surveys were administered in Siuna, Rosita, and Bonanza, Nicaragua between July 2019 and September 2020. Survey participants were aged 18-years or older that live in communities served by the Ministry of Health. The surveys were open response and multiple-choice format. Surveys included questions about structural/cultural/financial barriers to care, communication barriers, and knowledge of healthcare services. Data was managed using REDCap tools and analysis was completed using R. Individuals that previously visited a health post were significantly more likely to have a positive perception of surgery compared to those who had not (OR = 1.4) (p = 0.019). This finding remained significant after adjustment for education, age, and municipality. However, previous hospital visits did not have a significant impact on perception of surgery. Individuals with higher transportation costs reported a negative perception of surgery (40.4%), as well as those who used private transportation (29.1%) (p<0.001). Participants that reported travel obstacles were 2.64 times as likely to have a positive perception of surgery (p<0.001), even when adjusted for all demographics except site. These findings suggest that individuals who previously interacted with only lower-level healthcare environments were significantly more likely to have a positive perception of surgery. Counterintuitive findings show that access to public transport, transportation costs >2USD, and cell-phone usage increased negative perception of surgery. This study demonstrates the complexity of variables that impact perceptions of healthcare services while highlighting areas of focus for future targeted investments.

A Distributed VMD-BiLSTM Model for Taxi Demand Forecasting with GPS Sensor Data.

With the ubiquitous deployment of mobile and sensor technologies in modes of transportation, taxis have become a significant component of public transportation. However, vacant taxis represent an important waste of transportation resources. Forecasting taxi demand within a short time achieves a supply-demand balance and reduces oil emissions. Although earlier studies have forwarded highly developed machine learning- and deep learning-based models to forecast taxicab demands, these models often face significant computational expenses and cannot effectively utilize large-scale trajectory sensor data. To address these challenges, in this paper, we propose a hybrid deep learning-based model for taxi demand prediction. In particular, the Variational Mode Decomposition (VMD) algorithm is integrated along with a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the prediction process. The VMD algorithm is applied to decompose time series-aware traffic features into multiple sub-modes of different frequencies. After that, the BiLSTM method is utilized to predict time series data fed with the relevant demand features. To overcome the limitation of high computational expenses, the designed model is performed on the Spark distributed platform. The performance of the proposed model is tested using a real-world dataset, and it surpasses existing state-of-the-art predictive models in terms of accuracy, efficiency, and distributed performance. These findings provide insights for enhancing the efficiency of passenger search and increasing the profit of taxicabs.

Assessing the evolution of Public Transportation demand over time based on real data through survival analysis in Bologna, Italy

Assessing the evolution of Public Transportation demand over time based on real data through survival analysis in Bologna, Italy

Study on the Electronic Design Automation of Taximeter

The integration of Field Programmable Gate Arrays (FPGAs) into the design of digital taximeters signifies a transformative step forward in the field of Electronic Design Automation (EDA), catering to the evolving needs of urban transportation systems. This paper details the development and implementation of an FPGA-based digital taximeter that aims to enhance fare calculation accuracy, operational reliability, and adaptability to regulatory changes through the use of sophisticated technology. FPGAs are chosen for their ability to perform high-speed processing and to be reprogrammed in the field, which allows the taximeter system to adapt quickly to new fare structures and transportation regulations.

Examining the relationship between the built environment and carbon emissions from operating vehicles: enlightenment from nonlinear models.

Carbon emissions from urban transportation significantly contribute to overall transportation emissions and are a major cause of the continuous rise in global temperatures. Understanding the spatial distribution and influencing factors of carbon emissions from operating vehicles can aid in formulating targeted policies and promoting emission reduction. To analyze the factors influencing urban traffic carbon emissions, we calculated emissions using trajectory data from operating vehicles in Shenzhen. We then used gradient boosting regression tree methods, specifically RF, XGBoost, and LightGBM models, to analyze the impact of the built environment on vehicle emissions. We used the XGBoost model for detailed factor analysis by comparing the models. The results indicate that bus stops, intersections, housing density, metro stops, and land use mix are the top five factors influencing emissions. When road density is 0-15 km/km2, the distance from the city center is 0-6 km, and the population exceeds 2000/km2, the built environment significantly reduces vehicle emissions.

Access to bus services: an analysis of the influence of land use types

ABSTRACT During travel, commuters are naturally influenced by built environment factors, particularly land use types. However, the existing literature overlooks this impact and neglects the application of the adjusted Gaussian two-step floating catchment area method in studying accessibility to metro. To analyse the influence of land use types on accessibility to transit and the nature of such influence, we employed the adjusted Gaussian two-step floating catchment area method in measuring accessibility and the entropy index in gauging land use diversity. We also deduced the changes in accessibility scores to capture the effects of land use types. Additionally, we examined the differences in the bus and metro accessibility scores to clarify the influence of each mode. The accessibility scores in the study area were influenced by the land use types within it, and this influence was significant in the core administrative districts. These findings will support policymakers in land use and public transport planning.

Changes in Fuel Prices and the Use of Public Transport: Insights from the European Union following the Invasion of Ukraine

Rising fuel prices are known to affect public transport (PT) demand. However, the impact of the oil market inflation and subsequent energy crisis following the 2022 Russian invasion of Ukraine on mobility behaviour in Europe has received little attention. This study examines shifts in fuel prices and PT mobility across 25 Europe-an Union countries between February and May 2022. Weekly variations in PT mobility were correlated with diesel and gasoline prices, and a multiple linear regression was conducted to assess the influence of contextual factors in explaining variations in PT usage. The results indicate a significant increase in PT mobility across all countries after February 2022, which was positively associated with rising fuel prices. Approximately 84% of the variance in mobility was explained, with more pronounced increases observed in countries characterised by lower telecommuting prevalence, higher housing costs relative to household income, greater price differentials between gasoline and diesel, lower adoption of alternative fuels, and higher motorisation rates. These findings suggest that the surge in fuel prices, driven by the 2022 energy crisis, may have stimulated an increase in transit ridership.

Towards socially equitable public transport systems: The effect of COVID-19 on taxi trip behavior

The COVID-19 pandemic emerged as a very influential occurrence with a profound impact on a global scale. The onset of the pandemic abruptly disrupted the regular course of everyday activities, primarily impacting urban regions. Hence, it is imperative to understand the effects of the COVID-19 pandemic on modern urban areas. This study seeks to analyze the effect of the pandemic on travel behavior by utilizing GPS data obtained from taxis, with a specific focus on spatial socioeconomic features. The M2 metro line in Istanbul has been selected for evaluation. In this analysis, four distinct periods are considered: total, off-peak, morning, and evening peaks. The stations are categorized using K-means clustering. The estimation models are constructed using ordinary least squares (OLS), spatial autoregression (SAR), and geographically weighted regression (GWR) techniques, which are applied to the variation in daily average cab trips and the characteristics of stations. The GWR models provide superior performance in comparison to the other two models, with notable distinctions observed in peak times, particularly morning peak when compared to total and off-peak counts. The findings indicate that factors such as population, population density, socioeconomic status, and the quantity of shopping malls are influential variables in elucidating and forecasting the fluctuations in taxi trip counts.

Walk-centric deliberations

This article outlines methods of practice-oriented teaching at universities integrating democracy, sustainability, and learning in adult education.. The methods highlight the potential for democratic innovation in local communities in which diverse perspectives come together to address societal challenges. With a focus on sustainability and the connection among place, space, and pedagogy it introduces ‘Voice-Resonance Walks,’ a deliberative method piloted in Stuttgart, Germany, for social work education. It also highlights pilot deliberations at the district (Mini-Studentlab Deliberative Walks) and cityscape (Public Transport Walks) levels in Hamburg, Germany, involving interdisciplinary student groups in lifelong learning. These examples illustrate how higher education institutions are engaging with the public sphere, with a particular focus on vulnerable groups of citizens and on fostering civic learning. They aim to equip students with the knowledge and skills to become future experts, decision-makers, and active citizens, capable of understanding the challenges facing both their local neighborhoods and diverse urban spaces.

Transport Safety and Quality of Life in Developing Cities: An Analytical Study of Amman's Urban Transport System

Transport safety is crucial for social sustainability, especially in rapidly urbanizing developing cities. This study evaluates the safety of Amman, Jordan's urban transport system, and its impact on residents' quality of life. Using the Analytic Hierarchy Process (AHP), we identified and prioritized five key safety indicators: accident rates, fatalities and injuries, pedestrian safety, public perceptions of safety, and the presence of safety infrastructure. The findings revealed high accident rates, inadequate pedestrian infrastructure, and poor public perceptions of safety. These issues lead to high-stress levels, limited mobility, and increased reliance on private vehicles, exacerbating traffic congestion and environmental pollution. Recommendations include stricter road safety measures, upgraded pedestrian infrastructure, public awareness campaigns, and enhanced public transport services. This research highlights the need for coordinated efforts among government agencies, private sector partners, and the community to improve transport safety and quality of life in developing cities.

The Impact of Transport Costs on Business Decision-Making in Rural Areas

Mobility challenges in rural areas can have a negative impact on economic and demographic development. People in these areas often have to move to urban areas to find quality jobs or services, which can lead to a decline in population, and economic activity. In this paper we analyze the factors influencing business decisions in rural areas. Public transport infrastructure based on short energy cycles using solar energy represents a solution to boost business decision management in rural areas.

Study on the Optimization of Urban Traffic and Spatial Structure in the Environment of Low-Carbon Eco-Cities

Under the concept of “dual-carbon development”, optimising the spatial structure of urban life, perfecting the management system of public transport services, realising the spatial optimisation of urban transport, minimising carbon emissions and reducing environmental pollution are the problems that need to be solved urgently. This paper firstly analyses the development status quo of low-carbon cities, clarifies the influencing factors of low-carbon eco-cities, and discusses the correlation between the scale, population density, traffic structure and energy development of low-carbon eco-cities and low-carbon eco-cities. On this basis, the low-carbon ecological development strategy of City C in China is proposed as an example. Therefore, this study argues that low-carbon cities, as a strategic goal of new urbanisation development, should focus on constructing a theoretical system of urban upgrading and optimisation under the environment of green and sustainable ecological development, and introduce corresponding construction guarantee optimisation policies.

Decoupling Online Ride-Hailing Services: A Privacy Protection Scheme Based on Decentralized Identity

Online ride-hailing services have become a vital component of urban transportation worldwide due to their convenience and flexibility. However, the expansion of their user base has dramatically heightened the risks of user privacy information leakage. Among these risks, the privacy leakage problem caused by the direct correlation between user (driver and passenger) identity information and location-based ride information is of particular concern. This paper proposes a novel privacy protection scheme for ride-hailing services. In this scheme, decentralized identities are employed for user authentication, separating the identity registration service from the ride-hailing platform, thereby preventing the platform from obtaining user privacy data. The scheme also employs a fuzzy matching strategy based on location Points of Interest (POI) and a ciphertext-policy attribute-based hybrid encryption algorithm to hide the user’s precise location and restrict access to location information. Crucially, the scheme achieves the complete decoupling of identity registration services and location-based ride services on the ride-hailing platform, ensuring that users’ real identities and ride data cannot be directly associated, effectively protecting user privacy. Within the decoupled architecture, regulatory authorities are established to handle emergencies within ride-hailing services. Through simulation experiments and security analysis, this scheme is demonstrated to be both feasible and practical, providing a new privacy protection solution for the ride-hailing industry.