Search Results (195)

Implementing effective cycling mobility requires infrastructure that enhances safety and reduces travel time. A common metric for tracking progress is the total length of dedicated cycling infrastructure. However, this does not always correlate with increased cycling usage. For instance, in Italy (2008–2015), cycling infrastructure grew by 48%, but ridership remained unchanged. Design quality and behavioral and contextual factors all influence this dynamic. This study analyzes a 16-year time series (2009–2024) of monthly cyclist flows surveys in Bologna, Italy. It focuses on flows, gender, and bike lane usage. It represents the most detailed and longest series of its kind in the country. The findings show a positive correlation between infrastructure growth (meters per inhabitant) and cyclist flows, though this weakened significantly after COVID-19 and the extensive introduction of non-exclusive bike lanes on mixed-use roads from 2020. Regression analyses reveal that new bike flows per new meter/inhabitant of infrastructure were 3 times greater before 2020. This study identifies two likely causes: the insufficient perceived safety of the newly introduced mixed-traffic lanes from 2020 and the lack of attractivity of cycling for the female population, as highlighted in the decreasing trend in the usage of bike infrastructure by female riders after 2020. Full article

Promoting demand-responsive transit (DRT) is crucial for developing sustainable and green transportation systems in urban areas, especially in light of decreasing transit ridership and increasingly varying demand. However, the effectiveness of such services hinges on their ability to efficiently match varying travel demand. This paper presents a data-driven framework for the joint optimization of customized bus routes and timetables, to enhance both service quality and operational sustainability. Our approach leverages large-scale taxi trip data to identify latent travel demand, applying a spatial–temporal clustering method to group trip requests and identify DRT stops by trip origin, destination, and direction. An adaptive large neighborhood search (ALNS) algorithm is improved to co-optimize passenger waiting times and bus operation costs, where an unbalanced penalty for early or late schedule deviations is developed to better reflect passengers’ discomfort. The framework’s performance is validated through a real-world case study, demonstrating its ability to generate efficient routes and schedules. The model manages to improve passenger experience and reduce operation costs. By creating a more appealing and efficient service, this model contributes directly to the goals of green transport in terms of reducing the total vehicle kilometers that are traveled, and demonstrating a viable, high-quality alternative to private car usage. This study offers a practical and robust tool for transit planners to design a next-generation DRT system that is both economically viable and environmentally sustainable. Full article

Public transit demand forecasting is a foundational component of sustainable urban mobility, enabling efficient operation, equitable service provision, and planning of public transit systems. Urban imagery, such as aerial images, contains rich information about urban sociodemographic characteristics and the built environment, offering particular value for data-scarce regions where conventional datasets are limited or outdated. However, there is limited research on using these images for public transit demand forecasting. This study introduces a deep learning approach for predicting transit ridership using aerial images. The method employs an encoder–decoder architecture to functionally separate image-derived latent representations into sociodemographic and physical environment vectors, which are subsequently used as inputs to a neural network for ridership prediction. Using data from Seoul, South Korea, the effectiveness of the proposed method is evaluated against three baseline configurations. The results show that the sociodemographic latent vector captures spatially organized residential characteristics, while the physical environment vector encodes distinct urban landscape patterns such as dense housing, traditional street grids, open spaces, and natural environments. The proposed model, which uses only imagery-derived latent features, substantially outperforms the pure image baseline and narrows the performance gap with census-informed models, reducing sMAPE by 25–60% depending on the mode. Combining imagery with census variables yields the highest accuracy, confirming their complementary nature. These findings highlight the potential of imagery-based approaches as a scalable, cost-efficient, and sustainable tool for data-driven transit planning. Full article

Bike-sharing services contribute to reducing emissions and conserving natural resources within urban transportation systems. They also promote public health by encouraging physical activity and generate economic benefits through shorter travel times, lower transportation costs, and decreased demand for parking infrastructure. This paper examines the use of shared micro-mobility services in the Italian cities of Florence and Bologna, based on an analysis of GPS origin–destination data and associated temporal coordinates provided by the RideMovi company. Given the still-limited number of studies on free-floating and electric-scooter-sharing systems, the objective of this work is to quantify the performance of electric bikes and e-scooters in bike-sharing schemes and compare it to traditional, muscular bikes. Trips were reconstructed starting from GPS data of origin and destination of the trip with a shortest path criteria that considers the availability of bike lanes. Results show that e-bikes are from 22 to 26% faster on average with respect to muscular bikes, extending trip range in Bologna but not in Florence. Electric modes attract more users than traditional bikes, e-bikes have from 40 to 128% higher daily turnover in Bologna and Florence and e-scooters from 33 to 62% higher in Florence with respect to traditional bikes. Overall, turnover is fairly low, with less than two trips per vehicle per day. The performance is measured in terms of trip duration, speed, and distance. Further characteristics such as daily turnover by transport mode are investigated and compared. Finally, spatial analysis was conducted to observe demand asymmetries in the two case studies. The results aim to support planners and operators in designing and managing more efficient and user-oriented services. Full article

Evaluating transit-oriented development (TOD) efficiency in metro station areas remains challenging, as the traditional “Node–Place” model gives limited consideration to guiding factors and struggles to account for inter-regional flows under spatial heterogeneity. To address these limitations, this study develops an enhanced “Node–Place–Accessibility” model by introducing an accessibility dimension to better capture station-level connectivity and walkability. DepthmapX and a convex space approach were applied to quantify station-area accessibility, reflecting passengers’ perceived spatial distance during transfers. The model establishes a TOD measurement framework based on spatial coupling and functional connectivity, enabling the identification of factors influencing metro ridership across different spatial scales. Moran’s I was employed to describe spatial agglomeration and a local spatial clustering method integrating both passenger flow and built-environment (BE) characteristics was constructed to reveal differentiated spatial patterns. The Multiscale Geographically Weighted Regression (MGWR) model was further employed to quantify the spatially varying impacts of BE factors on ridership. Results indicate that the improved model provides stronger discriminative power in identifying “balanced stations,” and that BE conditions exert significant impact on metro ridership, particularly in areas with strong coordination among TOD components. Among the BE dimensions, design granularity exerts a more substantial impact on ridership than connectivity, density, and accessibility. This methodology provides large cities with a reliable tool for formulating targeted strategies that promote positive interactions between transportation and land use, thereby supporting sustainable urban development. Full article

The effectiveness of transit-oriented development (TOD) in achieving emission reductions and energy savings is highly influenced by policy frameworks, the accessibility of sustainable transport systems, and the degree of land use integration. This study investigated the implementation of TOD in Dukuh Atas along the Sudirman–Thamrin corridor in Jakarta to assess its role in promoting energy efficiency and lowering emissions. The analysis incorporated carbon emission calculations, annualized traffic volumes, and emissions data, alongside land use metrics such as the floor area ratio (FAR), job-to-housing ratio, and point-of-interest (POI) density. The findings indicate that while TOD implementation in the corridor is still evolving, there were positive outcomes in several key areas. Energy efficiency measures have been partially realized through the operation of electric buses in the bus rapid transit (BRT) system, electrified rail modes, such as commuter lines, mass rapid transit (MRT), and light rail transit (LRT), and improved pedestrian infrastructure, as reflected in a favorable Pedestrian Environmental Quality Index (PEQI). Public transport ridership has significantly increased, contributing to a measurable reduction in emissions from private vehicle use. The land use analysis showed that medium- to high-density housing dominated (78.94% FAR), with a job-to-housing ratio of approximately 1:2. This study also found that the emission estimates were moderately sensitive to changes in both emission factors (EFs) and vehicle kilometers traveled (VKT). Overall, the results suggest that TOD can effectively contribute to energy savings and emission reductions by enhancing public transport usage and reducing dependence on motorcycles. Moreover, the efficacy of modal shifting in the Global South is significantly influenced by population mobility characteristics, which are intricately linked to socio-cultural factors, alongside government initiatives to improve the quality of mass public transportation systems (e.g., integration, availability, service coverage, affordable fares, and inclusive design). Full article

As global urbanization accelerates, cities increasingly shape economic growth and environmental outcomes, making sustainable urban and transport planning critical. Sustainable city branding (SCB) is emerging as a strategic tool that not only enhances a city’s global competitiveness but actively drives urban sustainability by integrating environmental, social, and economic dimensions aligned with the UN Sustainable Development Goals (SDGs). However, the direct link between SCB and transport planning remains largely unexplored, limiting actionable policy. This study introduces a novel conceptual framework connecting SCB with transport planning, positioning public transportation as a key lever for sustainable urban development. It identifies core interactions between city branding and sustainable mobility, proposes methodologies to evaluate SCB effectiveness, and addresses potential risks, challenges, and research gaps. A policy roadmap for decision-makers based on the framework is outlined. This roadmap is structured into three phases spanning a five-year program. In Phase 1, cities should lay the foundation by integrating SCB into municipal transport and sustainability plans and establishing measurable indicators aligned with the SDGs. Phase 2 focuses on engagement and experimentation, encouraging the creation of participatory branding platforms and the implementation of pilot projects, such as green mobility corridors or climate-resilient transit hubs. Finally, Phase 3 emphasizes monitoring and scaling, utilizing digital technologies for real-time tracking, evaluating pilot outcomes, and expanding successful initiatives based on key performance indicators, including ridership growth, carbon reduction, and citizen engagement. By linking SCB explicitly to transport planning and providing a concrete roadmap, this study offers a unique contribution to both urban sustainability research and practical policy-making, enabling cities to simultaneously strengthen their brand, enhance mobility, and achieve measurable sustainability outcomes. Full article

Urban rail transit, as a green, environmentally friendly, safe, and efficient mode of transportation, plays a crucial role in urban sustainable development. However, the influencing mechanism of build environment factors on rail transit ridership still needs to be further investigated. Also, the interaction effects between these factors have not been considered. This study aims to explore the relationship and impact of built environmental factors on metro ridership. The research employs the Multiscale Geographically Weighted Regression (MGWR) model to analyze the temporal and spatial effects of built environmental factors on the rail transit ridership. The GeoDetector model is utilized to investigate the interactive effects of these factors on rail transit ridership. The Shanghai Metro ridership data and built environment data are applied to validate the model. Based on data analysis results, we found that Food & Beverages and Accommodation services, respectively, have the greatest impact on metro ridership on weekdays and weekends. Furthermore, the interaction effects between other variable and Land use diversity significantly enhance rail transit ridership, validating the promoting effect of land use diversity on metro ridership. By proposing recommendations for relevant urban planning and policy formulation, we can foster the sustainable development of urban rail transit. Full article

Public transport bus services often experience financial inefficiencies due to high operational costs and unbalanced service allocation. To address these challenges, this study presents a machine learning-based framework aimed at optimizing financial and operational performance in public bus systems. A dataset comprising 57 routes including cost, service, and ridership data was analyzed to identify key factors correlated with net revenue. These features were integrated into multiple predictive models, among which support vector regression (SVR) with a Gaussian kernel and Bayesian optimization achieved the highest accuracy (R² = 0.99), indicating excellent generalization capability. Scenario simulations using the trained SVR model evaluated the effects of service and cost adjustments. Results showed that cutting personnel costs had the most significant effect on net income, followed by administrative and financial expenses. These findings highlight the importance of data-driven strategies such as route reallocation and workforce optimization. The proposed framework offers transit agencies a robust tool for improving efficiency and ensuring financial sustainability. Full article

Transportation systems are particularly vulnerable to disruptions such as pandemics, which create significant challenges for maintaining efficiency, safety, and service quality. This study focuses on rail passenger transport in the Slovak Republic and develops a simulation framework to evaluate system performance under crisis conditions. Weekly data from the national rail operator for the period 2019–2021 were combined with information on governmental restrictions, standardized into a five-level framework. A nonlinear autoregressive model with exogenous inputs (NARX), implemented and validated in MATLAB R2021b (MathWorks, Natick, MA, USA), was applied to simulate the impact of restrictive measures on passenger demand. The results revealed a strong relationship between the severity of measures and ridership levels, with the most significant effects observed in education, workplace access, movement limitations, and retail. For instance, during complete school closures, passenger volumes declined by up to 75% relative to the pre-pandemic baseline. Based on the simulation outcomes, recommendations were formulated for adapting railway operations, including dynamic adjustments of transport capacity (10–40%) according to restriction levels. The proposed modelling and simulation approach offers transport authorities a cost-effective tool for scenario testing, disruption management, and the design of resilient passenger rail systems capable of adapting to crises and uncertainties. Full article

A major challenge for modern transit systems relying on traditional fixed-route designs is providing broad accessibility to users. Flex-route transit can enhance accessibility in low-density areas, since it combines the directness of fixed-route transit with the coverage of on-demand mobility. Although deviating for optional pickups can increase ridership and transit accessibility, it also deteriorates the service performance for fixed-route riders. To balance this inherent trade-off, this paper proposes a reinforcement learning approach for deviation decisions. The proposed model is used in a case study of a proposed flex-route service in the city of Boston. The performance on competing objectives is evaluated for reward configurations that adapt to peak and off-peak scenarios. The analysis shows a significant improvement of our method compared to a heuristic derived from industry practice as a baseline. To evaluate robustness, we assess performance across scenarios with varying demand compositions (fixed and requested riders). The results show that the method achieves greater improvements than the baseline in scenarios with increased request ridership, i.e., where decision-making is more complex. Our approach improves service performance under dynamic demand conditions and varying priorities, offering a valuable tool for smart cities to operate flex-route services. Full article

Frequent transit services in urban areas have the potential to increase their accessibility to transit-dependent riders and reduce congestion by attracting new ridership through a modal shift. However, bus services operating in mixed traffic face operational challenges that reduce reliability and hinder their attractiveness. The sources of unreliability can range from local-level conditions, like the road infrastructure, to higher-level decisions, like the service plan. For the effective planning of improvement strategies, both scales of analysis must be considered. This paper uses a novel modeling framework to understand reliability by analyzing the route and segment factors separately. The Chicago Transit Authority (CTA) bus network is used as a case study for the analysis. The data reflect the operational, demand, and urban conditions of 50 high-frequency bus routes. At the route level, we use the coefficient of headway variation as the dependent variable and diverse route characteristics as explanatory variables. The results indicate that the most significant contributors to the variability of headways are variability in schedules and dispatching at terminals. It is also found that driver experience impacts reliability and that east–west routes are more unreliable than north–south routes. At the segment level, we use data from trips involved in bunching and gaps. As the dependent variable, a novel measure is formulated to capture how quickly bunching or gaps are formed. The bunching and gap events are treated as separate regression models. Findings suggest that link and dwell time variability are the most significant contributors to gap and bunching formation. In terms of infrastructure, bus lane segments reduce gap formations, and left turns increase bunching and gap formations. The insights presented can inform improvements in service and transit infrastructure planning to improve transit level of service (LOS) and support the future of sustainable, smart cities. Full article

The integration of urban bus and subway services is critical for attracting passengers and for the sustainable development of public transit, as it helps to boost ridership with an extensive service that combines the attractions of buses and subways. To identify barriers in transferring from bus to subway or vice versa at different periods of the day, this research develops the popular evaluation indices found in the literature and revises them to reflect the most critical attributes of transfer quality. Thus, the deficiencies of transferring from subway to bus or vice versa are independently examined. Motivated by the changes in the indices at different periods, the day is divided into multiple periods. Then, dynamic transfer-volume-based TOPSIS is developed, instead of assigning index weights based on period sequence. The index weight is revised to emphasize the peak periods. Taking a case study in Suzhou, the barriers to inter-modal transfer are identified between subways and buses. It is found that subway-to-bus transfer quality is only one-third of that of bus-to-subway transfers due to the great changes in bus runs (19–45 vs. 14–26), lower bus coverage rates (0.42–0.47 vs. 0.50–0.55), and larger deviation of connected POIs (9.0–9.4 vs. 1.1–1.8), as well as the lower reliability of connected bus lines (0.3–0.47 beyond peaks vs. 0.58 and 0.96). Multi-faceted implementations are recommended for inter-modal subway-to-bus transfers and bus-to-subway transfers, respectively. The research provides insights on enhancing bus–subway transfer quality with finer detail into different periods, to encourage the loyalty of transit passengers with more stable and reliable bus as well as transit service. Full article

Security checks (SCs) at metro stations are regarded as an effective measure to address the heightened security risks associated with high ridership. Introducing SCs without exacerbating congestion requires a thorough understanding of their impact on passenger flow. Most existing studies were conducted where SCs are mandatory and fixed at certain locations. This study presents a method for advising the scale and placement for SCs under a more relaxed security setting. Using agent-based simulation with heterogeneous profiles for both inbound and outbound passenger flow, existing bottlenecks are first identified. By varying different percentages of passengers for SCs and locations to deploy SCs, we observe the influence on existing bottlenecks and suggest a suitable configuration. In our experiments, key bottlenecks are identified before tap-in fare gantries. When deploying SCs near tap-in fare gantries as seen in current practices, a screening percentage of beyond 10% could exacerbate existing bottlenecks and also create new bottlenecks at SC waiting areas. Relocating the SC to a point beyond the fare gantries helps alleviate congestion. This method provides a reference for station managers and transport authorities for balancing security and congestion. Full article

The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty’s metro, buses, and trolleybuses, concentrations of CO₂ and PM_2.5 often accumulate, elevating the risk of respiratory and cardiovascular diseases. This study investigates the air quality along three of the city’s busiest transport corridors, analyzing how the concentrations of CO₂, PM_2.5, and PM₁₀, as well as the temperature and relative humidity, fluctuate with the passenger density and time of day. Continuous measurements were collected using the Tynys mobile IoT device, which was bench-calibrated against a commercial reference sensor. Several machine learning models (logistic regression, decision tree, XGBoost, and random forest) were trained on synchronized environmental and occupancy data, with the XGBoost model achieving the highest predictive accuracy at 91.25%. Our analysis confirms that passenger occupancy is the primary driver of in-cabin pollution and that these machine learning models effectively capture the nonlinear relationships among environmental variables. Since the surveyed routes serve Almaty’s most densely populated districts, improving the ventilation on these lines is of immediate importance to public health. Furthermore, the high-temporal-resolution data revealed short-term pollution spikes that correspond with peak ridership, advancing the current understanding of exposure risks in transit. These findings highlight the urgent need to combine real-time monitoring with ventilation upgrades. They also demonstrate the practical value of using low-cost IoT technologies and data-driven analytics to safeguard public health in urban mobility systems. Full article