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• The impact of e-scooter sharing on the usage of bike sharing is explored using Chicago data. • The weekly usage of bike sharing in e-scooter sharing operation area declined by 10.2% due to the impact. • Usage of bike sharing by members and non-members declined by 4% and 34% respectively. • Bike sharing usage during workday peak hours and other times did not change and declined. As a new type of shared micromobility, e-scooter sharing first appeared in the United States and became popular worldwide. Considering e-scooter sharing and bike sharing have similar service attributes, the ridership of bike sharing may be affected by the introduction of e-scooter sharing. To date, studies exploring this impact are limited. In this study, we seek to analyze the impact of e-scooter sharing on the usage of bike sharing from trip data of e-scooter sharing and bike sharing in Chicago for a total of 30 weeks. We rely on a difference-in-differences modeling approach based on the propensity score matching method. We found that the average duration of e-scooter trips is shorter than that of bike trips. The introduction of e-scooter sharing reduced the overall bike sharing usage by 23.4 trips per week per station (10.2%). bike sharing usage of non-members and members decreased by 18.0 (34.1%) and 5.4 (4.0%) trips, and that of male and female members decreased by 3.3 (3.1%) and 2.0 (7.3%) trips, respectively. Furthermore, the volume of short-, medium-, and long-duration trips of bike sharing decreased by 10.9 (7.5%), 5.4 (9.6%), and 3.4 trips (20.5%), respectively. Finally, bike sharing use during non-peak hours decreased but was not affected during peak hours. [ABSTRACT FROM AUTHOR]

The ridesourcing service has taken a large portion of the taxi market in the past few years. Many studies have explored the influencing factors of traditional taxi and ridesourcing demand in different areas of the city. Few studies investigated the market share of ridesourcing in different regions of the city. Understanding the spatial distribution of the market share of ridesourcing service and the factors that determine this distribution could help government agencies understand the role that each type of service plays in the transportation system and evaluate the effect of different public policies on the two types of services. This paper studies this topic by constructing a panel fractional regression model based on the taxi trip data of New York City in 2017. Results show that the market share of the traditional taxi is declining while that of ridesourcing service is increasing. The market share of ridesourcing services is higher in remote areas, areas with low population density, low density of transportation facilities, low household income, and high proportion of young residents. It indicates that ridesourcing service could provide more equitable service by better serving the underserved areas while traditional taxi caters more to the high-demand areas. • We explore built environment factors influencing market share of ridesourcing (RS). • A panel fractional regression model is used to analyze taxi trip data of NYC. • Remote areas, high ratio of low income and young residents are positively related. • Density of population, bus stop, road, and mixed land use are negatively related. • Results could help us understand the interaction of RS and traditional taxi. [ABSTRACT FROM AUTHOR]

When estimating multinomial logit (MNL) models where choices are made from a large set of available alternatives computational benefits can be achieved by estimating a quasi-likelihood function based on a sampled subset of alternatives in combination with ' McFadden's correction factor '. In this paper, we theoretically prove that McFadden's correction factor minimises the expected information loss in the parameters of interest and thereby has convenient finite (and large sample) properties. That is, in the context of Bayesian estimation the use of sampling of alternatives in combination with McFadden's correction factor provides the best approximation of the posterior distribution for the parameters of interest irrespective of sample size. As sample sizes become sufficiently large consistent point estimates for MNL can be obtained as per McFadden's original proof. McFadden's correction factor can therefore effectively be applied in the context of Bayesian MNL models. We extend these results to the context of mixed multinomial logit models (MMNL) by using the property of data augmentation in Bayesian estimation. McFadden's correction factor minimises the expected information loss with respect to the augmented individual-level parameters, and in turn also for the population parameters characterising the shape and location of the mixing density in MMNL. Again, the results apply to finite and large samples and most importantly circumvent the need for additional correction factors previously identified for estimating MMNL models using maximum simulated likelihood. Monte Carlo simulations validate this result for sampling of alternatives in Bayesian MMNL models. • McFadden's correction factor in MNL minimises parameters' expected information loss. • McFadden's correction factor in MNL thereby has convenient finite sample properties. • Bayesian MMNL models using data augmentation only need McFadden's correction factor. • Results are confirmed using Bayesian MNL and MMNL models and Monte Carlo analysis. [ABSTRACT FROM AUTHOR]

Electric scooter (e-scooter) sharing systems (ESSs) have been widely adopted by many cities around the world and have attracted a growing number of users. Although some studies have explored the usage characteristics and effects of the built environment on ESS ridership using one city as an example, few studies have considered multiple cities to obtain generalizable and robust results. To fill this research gap, we collect the ESS trip data of five cities in the U.S., namely Austin, Minneapolis, Kansas City, Louisville, and Portland, and explore the effects of the built environment on ESS ridership after controlling for socioeconomic factors. The temporal distributions of e-scooter ridership of different cities are similar, having a single peak period on weekdays and weekends between 11:30 and 17:30. In terms of spatial distribution, the ESS ridership is higher in universities and urban centers compared to other areas. Multilevel negative binomial model results show that ESS trips are positively correlated with population density, employment density, intersection density, land use mixed entropy, and bus stop density in the census block group. E -scooter ridership is negatively correlated with the median age of the population in the census block group and distance to the city center. The findings in this article can help operators understand the factors that affect the ridership of shared e-scooters, determine the changes in ridership when the built environment changes, and identify high-ridership areas when ESS is implemented in new cities. • The effect of built environment on e-scooter ridership (ER) is studied. • Trip data of five cities are analyzed to obtain more generalizable and robust result. • Temporal variation of ER of different cities exhibits similar pattern. • ER increases with population, employment, intersection density, and mixed land use. • ER decreases with median age of the population and distance to the city center. [ABSTRACT FROM AUTHOR]

Shared bikes can help cities achieve carbon neutrality goals. Cleaning and disinfection are vital procedures of the maintenance of shared bikes, especially during the COVID-19 pandemic because shared bikes could be a transmission intermediary of viruses. This study proposes an optimization model of the cleaning and disinfection scheme of the dockless shared bikes. The disinfection is assumed to be performed at night, when the usage is lowest. By regarding the disinfection staff as traveling salesmen, the model is formulated as an extension of the Multidepot Multiple Traveling Salesman Problem (MDMTSP). The objective function is to minimize the total cost; which consists of the cost associated with the working time and per-capita cost of the disinfection staff. A heuristic algorithm combining k -means clustering and genetic algorithm (K-GA) is adopted to find the lower bound solution. Then, the K-GA-adjustment algorithm has been adopted to find the solutions that satisfy the constraints. To reduce the computing time needed, an approximate function for the lower bound of the optimal number of disinfection staff is obtained by constructing a Continuous Approximation (CA) model. A case study based on real location data of shared bikes in Chengdu, China, is performed to show how the maintenance department could adopt the optimization framework to design an efficient scheme to clean and disinfect the shared bikes. [ABSTRACT FROM AUTHOR]

Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study exploresspatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factros (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic,thus adjusting station locations, capacity, and rebalancing schemes accordingly. [ABSTRACT FROM AUTHOR]

• Eye tracker technology can improve efficiency of on-pack portion guidance. • The way people are asked to estimate portion size influences reliance on guidance. • For less familiar products, participants rely more on the provided serving guidance. • The sooner people notice guidance the more accurate their portion size estimates. A promising strategy to encourage portion control involves clear guidance and portion information on product packaging alongside nutrition details. However, literature on the impact of this information on serving sizes is conflicting. To gather more objective evidence about how consumers interact with on-pack portion guidance, we conducted a laboratory-based eye-tracker study with 66 participants completing two portion-size estimation tasks. In the first, participants estimated serving sizes indirectly by gauging how many people a product pack could serve. The second task involved direct portion size selection using pictorial representations of the food portion on a virtual plate. Additionally, we explored how participants' familiarity with products affected their portion estimations. Results: when portion guidance was presented, errors in portion estimation significantly decreased only in the indirect task, where 85% of participants showed an improved accuracy. This suggests that the way individuals are prompted to estimate portion sizes influences their approach and their reliance on the provided guidance. Higher error rates occurred when participants were more familiar with the products. For less familiar products, the lower portion estimation errors were associated with the longer fixations on the portion graphic in the indirect task. In addition, participants who quickly noticed the portion graphic provided more accurate portion size estimates. This emphasizes the practical importance of swiftly locating portion information on-pack to enhance estimation accuracy. [ABSTRACT FROM AUTHOR]

Direct ridership models can predict station-level urban rail transit ridership. Previous research indicates that the direct modeling of urban rail transit ridership uses different coverage overlapping area processing methods (such as naive method or Thiessen polygons), area analysis units (such as census block group and census tract), and various regression models (such as linear regression and negative binomial regression). However, the selection of these methods and models seems arbitrary. The objective of this research is to suggest methods of station-level urban rail transit ridership model selection and evaluate the impact of this selection on ridership model results and prediction accuracy. Urban rail transit ridership data in 2010 were collected from five cities: New York, San Francisco, Chicago, Philadelphia, and Boston. Using the built environment characteristics as the independent variables and station-level ridership as the dependent variable, an analysis was conducted to examine the differences in the model performance in ridership prediction. Our results show that a large overlap of circular coverage areas will greatly affect the accuracy of models. The equal division method increases model accuracy significantly. Most models show that the generalized additive models have lower mean absolute percentage errors (MAPE) and higher adjusted R 2 values. By comparison, the Akaike information criterion (AIC) values of the negative binomial models are lower. The influence of different basic spatial analysis unit on the model results is marginal. Therefore, the selection of basic area unit can use existing data. In terms of model selection, advanced models seem to perform better than the linear regression models. [ABSTRACT FROM AUTHOR]

Direct ridership models can predict station-level urban rail transit ridership. Previous research indicates that the direct modeling of urban rail transit ridership uses different coverage overlapping area processing methods (such as naive method or Thiessen polygons), area analysis units (such as census block group and census tract), and various regression models (such as linear regression and negative binomial regression). However, the selection of these methods and models seems arbitrary. The objective of this research is to suggest methods of station-level urban rail transit ridership model selection and evaluate the impact of this selection on ridership model results and prediction accuracy. Urban rail transit ridership data in 2010 were collected from five cities: New York, San Francisco, Chicago, Philadelphia, and Boston. Using the built environment characteristics as the independent variables and station-level ridership as the dependent variable, an analysis was conducted to examine the differences in the model performance in ridership prediction. Our results show that a large overlap of circular coverage areas will greatly affect the accuracy of models. The equal division method increases model accuracy significantly. Most models show that the generalized additive models have lower mean absolute percentage errors (MAPE) and higher adjusted R 2 values. By comparison, the Akaike information criterion (AIC) values of the negative binomial models are lower. The influence of different basic spatial analysis unit on the model results is marginal. Therefore, the selection of basic area unit can use existing data. In terms of model selection, advanced models seem to perform better than the linear regression models. [ABSTRACT FROM AUTHOR]

Direct demand modeling is a useful tool to estimate the demand of urban rail transit stations and to determine factors that significantly influence such demand. The construction of a direct demand model involves determination of the catchment area. Although there have been many methods to determine the catchment area, the choice of those methods is very arbitrary. Different methods will lead to different results and their effects on the results are still not clear. This paper intends to investigate this issue by focusing on three aspects related to the catchment area: size of the catchment area, processing methods of the overlapping areas, and whether to apply the distance decay function on the catchment area. Five catchment areas are defined by drawing buffers around each station with radius distance ranging from 300 to 1500 meters with the interval of 300 meters. Three methods to process the overlapping areas are tested, which are the naïve method, Thiessen polygon, and equal division. The effect of distance decay is considered by applying lower weight to the outer catchment area. Data from five cities in the United States are analyzed. Built environment characteristics within the catchment area are extracted as explanatory variables. Annual average weekday ridership of each station is used as the response variable. To further analyze the effect of regression models on the results, three commonly used models, including the linear regression, log-linear regression, and negative binomial regression models, are applied to examine which type of catchment area yields the highest goodness-of-fit. We find that the ideal buffer sizes vary among cities, and different buffer sizes do not have a great impact on the model's goodness-of-fit and prediction accuracy. When the catchment areas are heavily overlapping, dividing the overlapping area by the number of times of overlapping can improve model results. The application of distance decay function could barely improve the model results. The goodness-of-fit of the three models is comparable, though the log-linear regression model has the highest prediction accuracy. This study could provide useful references for researchers and planners on how to select catchment areas when constructing direct demand models for urban rail transit stations. [ABSTRACT FROM AUTHOR]

Understanding the relationship between the built environment and e-scooter sharing (ESS) usage is important because it could help planners determine the high-demand area and design an effective investment plan to promote the use of micromobility. Previous studies usually assume that the relationship is linear, which may lead to inaccurate ridership prediction and ineffective policy. Thus, this study explores the nonlinear and threshold effects of the built environment on ESS ridership in Los Angeles using the gradient boosting decision tree. Fourteen built environment and ten demographic variables are selected as independent variables. We find that the built environment accounts for 91.66% of the total relative importance. The ten most important variables are intersection density, road density, public transit station density, restaurant density, employment density, distance to the center, population density, proportion of park area, parking density, and bike lane density. The nonlinear and threshold effects of the built environment on ESS ridership are determined. By using two spatial analysis units (census tract and census block group) and four temporal analysis units (spring, summer, autumn, and winter), the modifiable areal unit problem and the modifiable temporal unit problem are revealed. [ABSTRACT FROM AUTHOR]