Bike sharing systems are adopted by many cities due to its contribution to energy saving and mitigation of traffic congestion. Understanding factors that influence bike sharing ridership and accurate ...estimation of ridership play an important role in designing the system. Previous studies assume the relationship between predicting variables and the response variable are the same across the study area. However, this assumption may not be true, since the study area is usually wide and thus the relationship between predicting variabels and the response variable may change across space. As a result, semi-parametric geographically weighted regression (S-GWR) model is used to explore the spatially varying relationship. S-GWR is an extension of the GWR model. While in GWR model, all predicting variables are local variables with spatially varying relationship with the response variable, S-GWR model allows predicting variables to be either global or local, which is closer to reality. We also extend previous studies by differenciating members and 24-h pass users, as well as data related to trip production and trip attraction. Results show that S-GWR models fit the data better and the relationship between some predicting variables and response variable are local while other relationships are global. Ridership of both members and 24-h users are positively related to number of employed residents nearby and capacity of the station, and negatively related to distance to central business area and percent of low-income workers living nearby. Number of employments is only significantly associated with trip attraction. Among them, the variable capacity is always a global variable, with higher capacity associated with higher ridership. As a result, S-GWR model could be used to estimate the ridership of stations for accurate prediction and spatially varying relationship between ridership and influencing factors should be considered when designing bike sharing system.
•S-GWR model is used to explore the spatially varying relationship between bike sharing ridership and influencing variables.•Trip production, trip attraction of members and all trips of 24-h users are modeled separately.•S-GWR model has the highest goodness-of-fit, followed by GWR model, and then OLS model.•Distance to CBD is a local variable and capacity of a station is global variable in all S-GWR models.•Number of employments is only significantly associated with trip attraction.
Many people criticize the inequity of traditional taxi (TT) services and believe the entry of ride-hailing taxis (RT) can address the issue. However, this has been understudied in the literature. ...This paper aims to estimate the equity of TT and RT services during peak hours and to study how the entry of RT affects equity by analyzing trip data of TT and RT in New York City in 2010 and 2017 (before and after the entry of RT). First, we used the Lorenz curve and the Gini coefficient to estimate the equity of taxi services against population and employment. The results show that the equity of RT in 2017 is higher than that of TT and the equity of TT + RT in 2017 is higher than that of 2010. Mixed geographically weighted regression (MGWR) was applied to determine whether the relationships between taxi trips and population/employment would vary across different taxi zones. The coefficient of variation (CV) of local coefficients of population/employment is used as an indicator of equity. Results show that RT services were more equitable than TT services in 2017 and that the overall taxi service in 2017 was more equitable than that of 2010.
Population aging has become a notable and enduring demographic phenomenon worldwide. Older adults’ walking behavior is determined by many factors, such as socioeconomic attributes and the built ...environment. Although a handful of recent studies have examined the influence of street greenery (a built environment variable readily estimated by big data) on older adults’ walking behavior, they have not focused on the spatial heterogeneity in the influence. To this end, this study extracts the socioeconomic and walking behavior data from the Travel Characteristic Survey 2011 of Hong Kong and estimates street greenery (the green view index) based on Google Street View imagery. It then develops global models (linear regression and Box–Cox transformed models) and local models (geographically weighted regression models) to scrutinize the average (global) and location-specific (local) relationships, respectively, between street greenery and older adults’ walking time. Notably, green view indices in three neighborhoods with different sizes are estimated for robustness checks. The results show that (1) street greenery has consistent and significant effects on walking time; (2) the influence of street greenery varies across space—specifically, it is greater in the suburban area; and (3) the performance of different green view indices is highly consistent.
As the most prevalent physical activity and transportation mode for older people, walking is considered to have multiple health and well‐being benefits. Previous studies used separate models to ...assess the built‐environment determinants of a battery of walking behavior measures, such as walking frequency and duration. In a departure from them, this study develops a system of equations, which is estimated by seemingly unrelated regression, to determine the built‐environment factors that significantly influence two correlated walking behavior measures (including walking frequency and duration) of older adults in Xiamen (a medium‐sized Chinese city) based on data from the Travel Survey of Xiamen Residents 2015 and built‐environment geo‐data. The results show the following: (1) the walking frequency and duration of older adults are affected by the built environment and socio‐demographic characteristics; (2) land‐use mix, intersection density, and bus route density positively influence older adults’ walking frequency and duration; (3) distance to the commercial center adversely impacts the walking frequency and duration; and (4) the built environment has similar effects on the two measures. This study offers a worthwhile reference for policy intervention to promote older adults' walking activities, thereby contributing to active and healthy aging.
The rapid adoption of electric bikes (e-bikes) (~150 million in 10 years) has come with debate over their role in China's urban transportation system. While there has been some research quantifying ...impacts of e-bikes on the transportation system, there has been little work tracking e-bike use patterns over time. This paper investigates e-bike use over a 6-year period. Four bi-annual travel diary surveys of e-bike users were conducted between 2006 and 2012 in Kunming, China. Choice models were developed to investigate factors influencing mode-transition and motorization pathways. As expected, income and vehicle ownership strongly influence car-based transitions. Younger and female respondents were more likely to choose car-based modes. Systematic and unobserved changes over time (time-dynamics) favor car-based modes, with the exception of previous car users who already shifted away from cars being less likely to revert to cars over time. E-bikes act as an intermediate mode, interrupting the transition from bicycle to bus and from bus to car. Over 6 years, e-bikes are displacing prospective bus (65→55%), car/taxi (15→24%) and bicycle (19→7%) trips. Over 40% of e-bike riders now have household car access so e-bikes are effectively replacing many urban car trips.
•About half of e-bike riders are substituted bus riders.•About one quarter of e-bike riders are substituted car users.•Most e-bike riders would not shift to bicycles.•E-bike policy (supportive/restrictive) should consider mode shift impacts.•High incomes and education levels dominate the transition to higher car use.
This study aims to investigate the effect of coronavirus disease 2019 (COVID-19) on the Chinese public's mental health during its early stage. We collected the data through an online questionnaire ...survey. Specifically, we adopted the impact of event scale-revised (IES-R) and state-trait anxiety inventory (STAI) to assess symptomatic responses to exposure to traumatic life events and public anxiety, respectively, in the COVID-19 pandemic outbreak. Then, we evaluated the differences in the scores among various socio-demographic groups using Kruskal-Wakkis H tests and
-tests and analyzed the IES-R, state anxiety (SA) score, and trait anxiety (TA) score using the Pearson correlation analysis. Finally, we conducted a path analysis to determine the mediating role of post-traumatic stress disorder symptoms (measured by the IES-R) in the relationship between TA and SA. The results show that the average of the SA and TA scores were 48.0 ± 10.4 and 38.0 ± 8.2, respectively; the respondents who suffered from mild, moderate, and severe psychological impacts because of the health crisis accounted for 21.9, 5.2, and 13.1%, respectively; farmers have the highest IES-R score than others; people with the highest income have the lowest SA level; a significant positive correlation existed between the IES-R and STAI scores; and TA produces both direct and indirect (through the IES-R) effects on SA. Overall, the general Chinese public exhibited much higher anxiety levels than normal in the early days of the pandemic outbreak. Accordingly, we strongly recommend psychological counseling and intervention support to mitigate the adverse psychological impacts of such an event.
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.
Along with the rapid development of Intelligent Transportation Systems, traffic data collection technologies have progressed fast. The emergence of innovative data collection technologies such as ...remote traffic microwave sensor, Bluetooth sensor, GPS-based floating car method, and automated license plate recognition, has significantly increased the variety and volume of traffic data. Despite the development of these technologies, the missing data issue is still a problem that poses great challenge for data based applications such as traffic forecasting, real-time incident detection, dynamic route guidance, and massive evacuation optimization. A thorough literature review suggests most current imputation models either focus on the temporal nature of the traffic data and fail to consider the spatial information of neighboring locations or assume the data follow a certain distribution. These two issues reduce the imputation accuracy and limit the use of the corresponding imputation methods respectively. As a result, this paper presents a Kriging based data imputation approach that is able to fully utilize the spatiotemporal correlation in the traffic data and that does not assume the data follow any distribution. A set of scenarios with different missing rates are used to evaluate the performance of the proposed method. The performance of the proposed method was compared with that of two other widely used methods, historical average and K-nearest neighborhood. Comparison results indicate that the proposed method has the highest imputation accuracy and is more flexible compared to other methods.
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.
This study aims to explore potential factors that affect non-motor vehicles (NMV) riders’ injury severity in accidents with motor vehicles (MV). Factors including human characteristics, vehicles, ...road features and environmental conditions were investigated. Police-reported crash data from the period of 2007 to 2017 in Xi’an, China were used to develop the generalized ordered logit models. Model results revealed that NMV riders’ injury severity was significantly influenced by rider’s Hukou, type of NMV, type of roadside protection, road structure, linearity, weather, accident time, light condition and visibility level. Besides, factors involving NMV rider’s age, compulsory third party insurance, type of MV, season, road type will increase their risk of suffering from serious/fatal injuries. Research findings are beneficial in helping policymakers to develop measures to improve NMV riders’ safety in China, such as isolating NMV and MV, educating drivers especially those from rural areas, and improving road and environment conditions.
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