Search Results (25)

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

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

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

Ridesharing is part of the innovative shared transport regime which aims to maximize the utilization of mobility resources. Gaining knowledge of ridesharing’s impacts and how to assess them can significantly improve such services and thus contribute to their adoption among broader groups of travelers and to travel behavior change. This paper presents the framework deployed for assessing the impacts of a first-/last-mile ridesharing pilot in Athens, Greece, and capturing stakeholders’ (i.e., a researcher organization, a public authority and an infrastructure provider) point of view about planning objectives. Four impact areas are defined in total, and Key Performance Indicators (KPIs) are used. In parallel, in order to understand the stakeholder priorities when designing ridesharing services, the Analytical Hierarchical Process is implemented to estimate weights for each impact area. Increasing rail ridership is considered the top priority for all stakeholders during the planning phase for a first-/last-mile ridesharing service, which may have various implications for future initiatives. In total, 28 participants used the ridesharing service as drivers and passengers during the demonstration period. Results show that although a ridesharing service is expected to be an asset in daily transport for city travelers, the technological constraints currently burden its usage. However, as supported by demo results and travelers’ experience, there is great potential of ridesharing to contribute to a sustainable transport system and serve as a first- and last-mile solution to public transport. Full article

The focus on sustainable transportation has increased interest in promoting sustainable modes of transport, such as rail. Understanding train passengers’ behaviors and perceptions is essential to enhance their travel experience and increase train ridership. Pre-boarding behaviors and perceptions are crucial in shaping the overall train travel experience. However, there are limited studies that have developed a systematic framework for investigating train passengers’ pre-boarding behaviors and perceptions. This paper examines the train passenger’s pre-boarding behaviors and perceptions about the station and platform. The study adopts a mixed-methods approach by developing a pre-boarding decision framework and combining it with questionnaire surveys to explore passengers’ behaviors and perceptions on the platform before boarding. A total of 429 valid responses from Melbourne metro train users were used for analysis. Descriptive statistics and correlation techniques were applied to identify patterns and relationships. The findings reveal common pre-boarding behaviors and perceptions. Furthermore, the study uncovers factors influencing these behaviors and perceptions, such as passenger demographics, travel patterns, and specific trip characteristics. For example, carrying large items and travel frequency significantly impact passengers’ travel experience in the pre-boarding phase. Waiting time, group travel, carrying small items, gender, and age group also significantly impact some pre-boarding behavior variables. Travel time, on the other hand, makes no significant impact on any of the pre-boarding variables that we examined. This research provides valuable insights for rail service operators and policymakers to enhance the pre-boarding experience, optimize station design, and improve passenger satisfaction. Full article

Background: People invest hours of their working day to pay for their vehicle. Is this a worthwhile use of their time? Wouldn’t public transport be a more economical solution for those on low income? Which demographic group typically chooses the most effective mode of transport? Aim: The two studies presented in this paper answer those fundamental questions based on the effective speed concept. This is calculated by dividing the distance travelled by time (i.e., minutes spent travelling and earning the money to afford it). Methodology: A digital model of the residents in Berlin is created to calculate the ‘effective speed’ for everyone. In the second study, based on a survey of residents in New York City, the best mode of transport for each respondent based on their effective speed has been identified and compared to the mode chosen. Results: The results of the Berlin case study show that the mode share for cars would be less than 6% if people choose the mode of transport with the highest effective speed. A similar picture is seen in NYC. Factors that significantly affect whether people who should use public transport, do so, include: age, gender, education, place of work or home. Full article

We simulate the introduction of shared, automated, and electric vehicles (SAEVs) providing on-demand shuttles service in a large-scale transport digital twin of the San Francisco Bay Area region (California, USA) based on transit supply and demand data, and using the mesoscopic agent-based Behavior, Energy, Autonomy, and Mobility beta software (BEAM) developed at the Lawrence Berkeley National Laboratory (LBNL). The main goal of this study is to test the operations of this novel mobility service integrated with existing fixed-route public transportation service in a mesoscopic simulation of a real case scenario, while testing the BEAM beta software capabilities. In particular, we test the introduction of fleets of on-demand vehicles bound to operate within circular catchment areas centered on high-frequency transit stops, with the purpose of extending the reach of fixed-route transit by providing an alternative first- and last-mile connection at high-frequency public transport stations. Results show that on-demand automated shuttles represent the best solution for some users, increasing the overall transit ridership by 3%, and replacing mostly ride-hail trips, especially those connecting to transit stops, but also some walking trips. This type of service has the potential to reduce overall vehicle miles traveled (VMT), increase transit accessibility, and save energy, but future research is needed to optimize this type of service and make it more attractive to travelers. Full article

As a form of rapid mass transportation, urban rail systems have always been widely used to alleviate urban traffic congestion and reconstruct urban structures. Land use characteristics are indispensable to this system and correlate with urban ridership. Dock-less bicycle-sharing expands the station service coverage range because it integrates public transportation with an urban rail system to create a convenient travel model. Consequently, the land use pattern with dock-less bicycle-sharing is associated with urban rail ridership. This paper measures the correlation between land use and urban rail ridership based on the trajectory of dock-less bicycle-sharing, which precisely reflects the travel behavior of passengers along the trip chain. The specific relationship has been determined using the random forest model. This paper found that the land use pattern could better explain the egress ridership during morning peak hours. In particular, it could explain 48.46% of the urban rail ridership in terms of egress, but the explicability for the ingress ridership slightly decreased to 36.88%. This suggests that the land use pattern is related to urban rail ridership. However, the impact situation varies, so we should understand this relationship with greater care. Full article

This paper investigates how the travel behavior relating to Public Transport (PT) changed during the COVID-19 pandemic, and which are the expectations about the extent of PT use post-pandemic. A revealed preferences questionnaire survey was distributed within an academic community in the city of Thessaloniki, Greece. To understand the factors potentially determining the future PT use, hierarchical ordered probit and bivariate ordered probit models were estimated. Results showed that the frequent PT users reduced by almost 75% during the pandemic. More than 29% of the local academic community members are reluctant to resume PT use at pre-pandemic levels. Non-captive users, teleworkers and those being unsatisfied with cleanliness and safety are less willing to travelling by PT post-pandemic. Female and under-stress passengers were found to particularly appreciate the use of facemasks and the increased service frequencies as post-pandemic policy measures. The study findings can inform the recovery strategies of transport authorities in order to retain the PT ridership at levels that will not threat the long-term viability of service provision. Future research may complement these findings by examining other population segments, such as the commuters and the elderly, under more advanced modelling techniques to account for additional unobserved behavioral patterns. Full article

Shared mobility is growing rapidly and changing the mobility landscape. The COVID-19 pandemic has complicated travel mode choice behavior in terms of shared mobility, but the evidence on this impact is limited. To fill this gap, this paper first designs a stated preference survey to collect mode choice data before and during the pandemic. Different shared mobility services are considered, including ride hailing, ride sharing, car sharing, and bike sharing. Then, latent class analysis is used to divide the population in terms of their attitudes toward shared mobility. Nested logit models are applied to compare travel mode choice behavior during the two periods. The results suggest that shared mobility has the potential to avoid the high transmission risk of public transport and alleviate the intensity of private car use in the COVID-19 context, but this is limited by anxiety about shared spaces. As the perceived severity of the pandemic increases, preference for ride hailing and ride sharing decreases, and a price discount for ride hailing is more effective than that for ride sharing at maintaining the ridership despite the impact of COVID-19. These findings contribute to understanding the change in travel demand and developing appropriate strategies for shared mobility services to adapt to the pandemic. Full article

With the emergence of ride-sourcing and ride-splitting services, more options are available to support shifts away from transit, where maintaining transit ridership increases requirements for transit service quality, so as to promote high-capacity and sustainable transport systems. In this endeavor, proper transit stop spacing is critical for both service accessibility and in-vehicle trip efficiency, as well as operation cost. This research explores acceptable stop spacing for three kinds of transit services from the perspective of travel behavior, drawing on the 2017 National Household Travel Survey in the United States. A stochastic frontier model is developed to infer passengers’ unobservable vertex of acceptable transit access times on the basis of observed walk time, which can be converted to the tolerance with respect to stop spacing with the average walking speed. Significant explanatory variables on the vertex of acceptable transit stop spacing are further identified with their quantified impacts, including household density, household income, wait time, trip distance, transfer, and maintenance purpose, while the inefficiency variance is significantly related to traveler age, wait time, secondary walk time, and trip frequency. Recommended response strategies follow. Findings from this study provide insights, guidelines, and implementation plans for different transit agencies when considering stop spacing redesign, to strengthen transit service appeal and to promote cooperative and sustainable multi-modal urban transport systems. Full article

Taxis are an important component of the urban public transportation system, with wide geographical coverage and on-demand services characteristics. Thorough understanding of the built environment affecting taxi ridership can enable transportation authorities to develop targeted policies for transportation planning. Previous studies in this field had few data sources and did not consider the spatiotemporal variability. This study aims to develop an analytical framework for understanding the spatiotemporal correlation between the urban built environment and taxi ridership, which is empirically analyzed in New York City. The built environment is defined through multisource data in terms of density, design, diversity, and destination accessibility. Besides the exploration of travel patterns, the spatiotemporal heterogeneity of taxi ridership is modeled using geographically and temporally weighted regression (GTWR). The result shows that GTWR outperforms ordinary least squares (OLS), geographically weighted regression (GWR), and temporally weighted regression (TWR) in both goodness of fit and explanatory accuracy. More importantly, our study found that land use diversity is negatively correlated with taxi ridership, while transportation diversity is positively correlated with it. A highly accessible road network improves the people’s demand for taxis in the morning rush hours. Moreover, the density of railway stations is positively correlated with taxi ridership on weekdays but adversely on weekends. These findings provide practical insights for urban transportation policy development and taxicab regulation. Full article

A bicycle-sharing system (BSS) has been implemented in Seoul, South Korea to promote green transportation policy as a Sustainable Development Goal (SDG) to mitigate climate change, reduce traffic jams, and promote physical activity. However, the concentration of air pollutants in Seoul often exceeds the standards of the World Health Organization, thereby creating a conflict with SDG 3 (Health). Therefore, it is important to recognize the trade-offs between actions targeted at SDGs as they might offset each other. In this context, a primary concern is investigating how the behavior of BSS users regarding outdoor air pollution appears. This study explores the relationship between ambient air pollution and the behavior of BSS riders in Seoul. We conducted a time-series analysis of associations between particulate air pollution and participation in the BSS. We used generalized additive models, adjusted for mean temperature, humidity, rainfall, day of the week, long-term trends, and seasonality to construct an exposure–response relationship. We observed a nonlinear relationship between increasing air pollution and bicycle ridership. This study method can be used as a basis for similar analyses to investigate BSS policies in other cities. Full article