Search Results (57)

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

Urban rail transit (URT) systems are critical for sustainable urban mobility but are increasingly vulnerable to disruptions and emergencies. While extensive research has examined the built environment’s influence on transit demand under normal conditions, the nonlinear causal mechanisms shaping URT passenger flow during emergencies remain understudied. This study proposes an artificial intelligence-based causal machine learning framework integrating causal structure learning and causal effect estimation to investigate how the built environment, network structure, and incident characteristics causally affect URT station-level ridership during emergencies. Using empirical data from Shanghai’s URT network, this study uncovers dual pathways through which built environment attributes affect passenger flow: by directly shaping baseline ridership and indirectly influencing intermodal connectivity (e.g., bus connectivity) that mitigates disruptions. The findings demonstrate significant nonlinear and heterogeneous causal effects; notably, stations with high network centrality experience disproportionately severe ridership losses during disruptions, while robust bus connectivity substantially buffers such impacts. Incident type and timing also notably modulate disruption severity, with peak-hour incidents and severe disruptions (e.g., power failures) amplifying passenger flow declines. These insights highlight critical areas for policy intervention, emphasizing the necessity of targeted management strategies, enhanced intermodal integration, and adaptive emergency response protocols to bolster URT resilience under crisis scenarios. Full article

Accurate and adaptive bus dispatching is vital for medium-sized urban centers, where static schedules often fail to accommodate fluctuating passenger demand. In this work, we propose a dynamic heuristic that integrates machine learning-based demand forecasts into a discrete-time planning horizon, thereby enabling real-time adjustments to dispatch decisions. Additionally, we introduce a tailored mathematical model—grounded in mixed-integer linear programming and space-time flows—that serves as a benchmark to evaluate our heuristic’s performance under the operational constraints typical of traditional public transportation systems in Colombian mid-sized cities. A key contribution of this research lies in combining predictive modeling (using Prophet for passenger demand) with operational optimization, ensuring that dispatch frequencies adapt promptly to varying ridership levels. We validated our approach using a real-world case study in Montería (Colombia), covering eight representative routes over a full day (5:00–21:00). Numerical experiments show that: 1. Our heuristic matches or surpasses 95% of the optimal solution’s operational utility on most routes, with an average gap of 4.7%, relative to the benchmark mathematical model. 2. It maintains high service levels—above 90% demand coverage on demanding corridors—and robust bus utilization, without incurring excessive operating costs. 3. It reduces computation times by up to 98% compared to the optimization model, making it practically viable for daily scheduling where solving large-scale models exactly can be prohibitively time-consuming. Overall, these results underscore the heuristic’s practical effectiveness in boosting profitability, optimizing resource use, and rapidly adapting to demand fluctuations. The proposed framework thus serves as a scalable and implementable tool for transportation operators seeking data-driven dispatch solutions that balance operational efficiency and service quality. Full article

The construction of urban rail transit plays a crucial role in improving traffic conditions in large cities, promoting green urban development, and reducing carbon dioxide emissions. Based on Chinese urban data, this paper employs a time-varying difference-in-difference model combined with the Heckman two-step method to control the sample selection problem. The objective of this methodology is to ascertain whether urban rail transit exerts a traffic creation effect or a traffic substitution effect. The following results were found: (1) Urban rail transit notably reduces the bus ridership per capita and the carbon dioxide emissions per capita in cities, a finding which passes a series of robustness tests, and the traffic substitution effect increases as the number of urban rail transit lines increases. (2) Heterogeneity analysis reveals that the traffic substitution effect in terms of carbon reduction in urban rail transit is greater in non-resource-based cities, cities with large carbon emissions, and cities with low fiscal pressure. (3) Urban rail transit reduces the carbon dioxide emissions per capita by improving the allocation efficiency of factor resources and further generating technological innovation and structural upgrading effects. (4) Spatial econometric analysis shows that urban rail transit has a significant spatial spillover effect on the reduction in carbon dioxide emissions per capita in neighboring cities. In short, urban rail transit can reduce the carbon dioxide emissions per capita by improving resource allocation and support the attainment of carbon peak and carbon neutrality goals. This effect is greater in large cities where urban rail transit networks have been established. Therefore, cities should actively promote the construction of metro and other rail transit within the scope of urban financial resources and make full use of the carbon reduction and efficiency enhancement functions of urban rail transit. In this way, urban rail transit can become an effective tool for the realization of sustainable development. Full article

The 20 min city concept ensures that essential services—such as work, education, healthcare, and recreation—are accessible within a 20 min walk or transit ride. This study evaluates urban accessibility in Ulaanbaatar by analyzing Points of Interest (POIs) and public bus transit networks using spatial analytics and deep learning techniques. Our finding highlights that geographical area characterization is a good proxy for predicting ridership in transit networks. For instance, healthcare and medical areas show a strong correlation with similar ridership behaviors. However, some areas lack nearby bus stations, leading to poorly placed transit stops with low walking scores. To address this, we propose the use of a Quad-Bus approach to identify optimal bus station locations in urban and suburban areas, considering amenity density and deep learning ridership models to diagnose and remedy accessibility gaps. This approach is evaluated using walking and transit scores for distances ranging from 5 to 20 min in the case of Ulaanbaatar city. Results show a moderate overall link between amenity density and ridership (r = 0.44), rising to 0.53 around healthcare clusters. However, >500 high-activity partitions contain no bus stop, and 40% of the city scores below 50 on a 0–100 walking index. Half of urban areas lack a stop within 300 m, leaving 60% of residents beyond a 10 min walk. Quad-Bus reallocations close many of these gaps, boosting walk and transit scores simultaneously. This research offers valuable insights for enhancing mobility, reducing car dependency, and optimizing urban planning to create equitable and sustainable 20 min city models. Full article

The efficient operation of urban bus systems necessitates the alignment of service supply with passenger demand. An inadequate supply of services results in passenger inconvenience, whereas excessive supply leads to inefficiencies for operators. This study introduces a performance measure to evaluate the equilibrium between demand and supply at bus stops. The methodology involves deriving cumulative distribution functions (CDFs) of passenger waiting times during peak (High Ridership Period, HRP) and non-peak hours (Non-High Ridership Period, NHRP) using smartcard data. The maximum vertical distance between these CDFs, along with their definite integrals, serves as the basis for the performance metric. Using a reference threshold of 0.16, bus stops are categorized into three groups: those experiencing excessive demand, those operating in a balanced state, and those with insufficient supply during non-peak hours. This method was applied to 1785 bus stops in Seoul, demonstrating that balanced stops exhibited the shortest average waiting times. The analysis also revealed that stops with excessive demand had significantly higher ridership, whereas stops with lower supply showed ambiguous boundaries between the HRP and NHRP. The proposed performance measure offers a valuable tool for assessing and enhancing the service levels of public transport systems. Full article

Population aging is a pressing global issue. As it progresses, older adults’ demand for public transport will increase. Ensuring their equitable access is vital for social equity. Meanwhile, physiological changes and travel preferences in older adults create unique bus usage patterns, making them more susceptible to the built environment. To test this, we compared bus travel behavior between older adults and young people in Wuhan, China. Our results showed that older adults travel more often, with a longer morning peak and less pronounced evening peak. We developed the GWRBoost model, combining Geographic Weighted Regression (GWR) and eXtreme Gradient Boosting (XGBoost), to explore the spatial heterogeneity and nonlinear impact of the built environment on bus travel for both groups. The study found significant differences in how the built environment affects bus ridership between older adults and young people. For older adults, proximity to the nearest bus stop is most critical, regardless of weekday or weekend. These variables also show spatial variations and nonlinear relations with bus ridership for both groups. These findings improve our understanding of older adults’ travel and offer insights for optimizing their travel environment and promoting transportation equity. Full article

This paper introduces a new geographically weighted nonlinear regression (GWNR) model to predict bus boarding more accurately. The proposed model, based on empirical data from selected bus routes in Chennai city, India, simultaneously accounts for spatial variations and non-linear relationships. The proposed GWNR model improves boarding forecast accuracy by increasing R² by 18.5% and reducing MAE by 15% compared to linear models. The results are used to identify best-fitting non-linear transformations for key variables such as bus and train station density, scheduled headway, and occupancy, thereby providing deeper insights and better interpretability. Unlike existing aggregate models, bus consideration probability is identified as a key predictor of bus boarding, thus reflecting non-users’ behavior. Without this effect, the influences of nearby bus and train stations show counterintuitive trends. Upon incorporating consideration probability, the presence of a single nearby train station increases bus boarding by improving accessibility, whereas multiple stations nearby reduce it due to competition effects. Finally, an illustrative policy application demonstrates the ability of the model to identify priority locations where scheduled headway changes are needed and to determine the optimal magnitude of adjustments. Such a targeted policy intervention is found to be twice as effective in increasing the ridership gain index compared to uniform area-wide policies. Full article

Bus lanes with intermittent prioritization (BLIPs) have been proposed as a way to reduce traffic burden and improve air quality along busy urban streets; however, to date, the impacts of BLIPs on local-scale air quality have not been thoroughly evaluated, due in part to challenges in study design. We measured traffic-emission proxies—black carbon aerosol and ultrafine particles—before and after the installation of a BLIP in the Boston area (Massachusetts, USA) in 2021, and compared our data with traffic measurements to determine whether changes in air quality were attributable to changes in traffic patterns. We used both stationary and mobile monitoring to characterize temporal and spatial variations in air quality both before and after the BLIP went into operation. Although the BLIP led to a reduction in traffic volume (~20%), we did not find evidence that this reduction caused a significant change in local air quality. Nonetheless, substantial spatial and temporal differences in pollutant concentrations were observed; the highest concentrations occurred closest to a nearby highway along a section of the bus lane that was in an urban canyon, likely causing pollutant trapping. Wind direction was a dominant influence: pollutant concentrations were generally higher during winds that oriented the bus lane downwind of or parallel to the highway. Based on our findings, we recommend in future studies to evaluate the effectiveness of BLIPs that: (i) traffic and air quality measurements be collected simultaneously for several non-weekend days immediately before and immediately after bus lanes are first put into operation; (ii) the evaluation should be performed when other significant changes in motorists’ driving behavior and bus ridership are not anticipated; and (iii) coordinated efforts be made to increase bus ridership and incentivize motorists to avoid using the bus lane during the hours of intermittent prioritization. Full article

It is essential to understand how the built environment affects transit ridership to prioritize public transit and make it more appealing, particularly in mid-sized cities on the Rust Belt due to the experience of population decrease and urban sprawl in the U.S. Although many studies have looked at factors that influence ridership, there is still a need for a methodological design that considers both route and environment characteristics for bus ridership. This study examined the daily ridership of 3794 bus stops across 57 routes in the Buffalo area of New York State and used random coefficients models to account for different levels of characteristics (bus stop level, route level, and transportation analysis zone (TAZ) level). The study found that bus frequency and bus stop centrality were positively correlated with ridership, while total route stops had a negative effect. By controlling the impact of bus routes, the study showed that the multi-level design using random coefficients models was more effective than traditional OLS and spatial lag models in quantifying the impact of bus routes and TAZs. These findings provide local policy implications for route design, bus operation, and transit resource allocation. Full article

Understanding the spatially varying effect mechanism of intermodal connection on metro ridership helps policymakers develop differentiated interventions to promote metro usage, especially for megacities with multiple city sub-centers and ring roads. Using multiple datasets in Shanghai, this study combines Light Gradient Boosting Machine (LightGBM) with Shapley additive explanations (SHAP) to explore these effects with the consideration of the built environment and metro network topology. Results show that the collective impacts of intermodal connection are positive, not only within the main city but also alongside the main commuting corridors, while negative effects occur in the peripheral area. Specifically, bike sharing trips increase metro ridership within the inner ring of the city, while bus services lower metro usage at stations alongside the elevated ring roads. Parking facilities enable metro usage at city sub-centers, and the small pedestrian catchment area increases metro riders alongside the main commuting corridors. Empirical findings help policymakers understand the effect mechanism of intermodal connection for stations in different regions and prioritize customized planning strategies. Full article

Bus transit plays a vital role in urban public transportation but often struggles to provide accurate and reliable departure times. This leads to delays, passenger dissatisfaction, and decreased ridership, particularly in transit-dependent areas. A major challenge lies in the discrepancy between actual and scheduled bus departure times, which disrupts timetables and impacts overall operational efficiency. To address these challenges, this paper presents a neural network-based approach for real-time bus departure time prediction tailored for smart IoT public transit applications. We leverage AI-driven models to enhance the accuracy of bus schedules by preprocessing data, engineering relevant features, and implementing a fully connected neural network that utilizes historical departure data to predict departure times at subsequent stops. In our case study analyzing bus data from Boston, we observed an average deviation of nearly 4 minutes from scheduled times. However, our model, evaluated across 151 bus routes, demonstrates a significant improvement, predicting departure time deviations with an accuracy of under 80 s. This advancement not only improves the reliability of bus transit schedules but also plays a crucial role in enabling smart bus systems and IoT applications within public transit networks. By providing more accurate real-time predictions, our approach can facilitate the integration of IoT devices, such as smart bus stops and passenger information systems, that rely on precise data for optimal performance. Full article

Dockless bike-sharing mobility brings considerable benefits to building low-carbon transportation. However, the operators often rush to seize the market and regulate the services without a good knowledge of this new mobility option, which results in unreasonable layout and management of shared bicycles. Therefore, it is meaningful to explore the relationship between the built environment and bike-sharing ridership. This study proposes a novel framework integrated with the extreme gradient boosting tree model to evaluate the impacts and threshold effects of the built environment on the origin–destination bike-sharing ridership. The results show that most built environment features have strong nonlinear effects on the bike-sharing ridership. The bus density, the industrial ratio, the local population density, and the subway density are the key explanatory variables impacting the bike-sharing ridership. The threshold effects of the built environment are explored based on partial dependence plots, which could improve the bike-sharing system and provide policy implications for green travel and sustainable transportation. Full article

An influx of new mobility trends such as fare-free bus transportation, ride hail, and e-scooter services to improve access and affordability of transportation on campus may be shifting the travel behavior of campus patrons such that it affects their long-term health outcomes. The main research questions explored in this study are as follows: (1) why university patrons choose new modes of travel?; (2) what existing mode did the new modes of travel replace for the riders?; and (3) is the average body mass index (BMI) of users primarily using non-motorized transit options lower than those using motorized or both (referred to as hybrid) for on-campus travel needs? An online survey was administered to a campus community (n = 3309) including students (48%), faculty (15%), and staff (37%) in fall of 2018 when fare-free bus transportation and e-scooters became available on campus, and a gradual increase in ridership of ride-hail services was simultaneously observed. This study found that campus patrons were more inclined to replace active modes of travel with affordable and accessible modes of transportation, thereby substituting their walking or biking routine with app-based transportation services. The mean BMI among travelers who chose motorized transportation modes was more than active travelers, and the BMI was statistically significantly associated with age, gender, race, class standing (undergraduate/graduate), and residence on/off campus. This study concludes with suggestions to prevent substitution of active with non-active transport choices and provides policy guidelines to increase awareness on achieving physical activity levels through active modes of travel for university patrons. Full article

Public transportation is an essential component of building sustainable communities. However, its ridership remains low in most cities in the United States. Among the major barriers is the long distance to the bus stops, called the first-mile problem. Using a stated preference survey among 1056 residents of El Paso, Texas, this study addresses this problem by estimating additional transit trips that can be expected from the implementation of hypothetical, free shuttles between one’s home and the closest bus stops. Participants reported 7.73 additional transit trips per week (469% increase from the current baseline), including 3.03 additional trips for work, 1.94 for daily errands, 1.64 for leisure or social, and 0.93 for exercise or sports. The percentage of transit non-users dropped from 77.6% (baseline) to 38.2%. With the free shuttle service, respondents would favor bus rapid transit more than regular buses (4.72 vs. 3.00 additional trips). Residents identifying as an existing transit user, being Hispanic/Latino, owning at least one automobile, living within 1 mile of a transit stop, and feeling safe while riding the bus would make significantly more transit trips due to the service. This study suggests that programs to address/reduce the first-mile problem could increase transit demand and, therefore, contribute to creating sustainable and more connected communities. Full article