Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros

Dockless bike-sharing is emerging as a convenient transfer mode for metros. The riding distances of bike-sharing to or from metro stations are defined as transfer distances between dockless bike-sharing systems and metros, which determine the service coverages of metro stations. However, the transfer distances have rarely been studied and they may vary from station to station. Therefore, this study aims to explore the influencing factors and spatial variations of transfer distances between dockless bike-sharing systems and metros. First, a catchment method was proposed to identify bike-sharing transfer trips. Then, the Mobike trip data, metro smartcard data, and built environment data in Shanghai were utilized to calculate the transfer distances and travel-related and built environment variables. Next, a multicollinearity test, stepwise regression, and spatial autocorrelation test were conducted to select the best explanatory variables. Finally, a geographically weighted regression model was adopted to examine the spatially varying relationships between the 85th percentile transfer distances and selected explanatory variables at different metro stations. The results show that the transfer distances are correlated with the daily metro ridership, daily bike-sharing ridership, population density, parking lot density, footway density, percentage of tourism attraction, distance from CBD, and bus stop density around metro stations. Besides, the effects of the explanatory variables on transfer distances vary across space. Generally, most variables have greater effects on transfer distances in the city suburbs. This study can help governments and operators expand the service coverage of metro stations and facilitate the integration of dockless bike-sharing and metros.     

An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai,China

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R² = 0.774) than the OLS regression model (Adjusted R² = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.     

Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic

The COVID-19 pandemic has led to a globally unprecedented change in human mobility. Leveraging two-year bike-sharing trips from the largest bike-sharing program in Chicago, this study examines the spatiotemporal evolution of bike-sharing usage across the pandemic and compares it with other modes of transport. A set of generalized additive (mixed) models are fitted to identify relationships and delineate nonlinear temporal interactions between station-level daily bike-sharing usage and various independent variables including socio-demographics, land use, transportation features, station characteristics, and COVID-19 infections. Results show: 1) the proportion of commuting trips is substantially lower during the pandemic; 2) the trend of bike-sharing usage follows an “increase-decrease-rebound” pattern; 3) bike-sharing presents as a more resilient option compared with transit, driving, and walking; 4) regions with more white, Asian, and fewer African-American residents are found to become less dependent on bike-sharing; 5) open space and residential areas exhibit less decrease and earlier start-to-recover time; 6) stations near the city center, with more docks, or located in high-income areas go from more increase before the pandemic to more decrease during the pandemic. Findings provide a timely understanding of bike-sharing usage changes and offer suggestions on how different stakeholders should respond to this unprecedented crisis.     

Car ownership and urban development in Chinese cities: A panel data analysis

Growth in car ownership has significant impacts on the use of urban space and management of urban environments, which makes it a topic of increasing interest especially for developing countries such as China. The dynamics of and factors influencing ownership in Chinese cities need careful investigation. Using fixed effects models applied to annual panel data (1994–2012; 293 cities) this study aimed to achieve the following: 1) assess the relationships between car ownership and average annual income per capita, population, built-up area, road area per capita, urban population density, number of taxis and bus passenger volume; 2) examine the variation of these relationships across geographical regions (East, middle, and West China) and city sizes (cities with small, medium, large, and super-large populations). The results showed that car ownership was positively associated with average annual income per capita, built-up area, road area per capita, urban population density, and number of taxis at the national level. All associations, except with the number of taxis, varied significantly across geographical regions. Built-up area, road area per capita, and number of taxis had different associations with car ownership depending on city sizes. The findings improve the understanding of relationships between car ownership and urban environments vis-a-vis variations in income and infrastructure per capita, population density, and transportation alternatives. These results have important policy implications for managing cars and health problems related to cars in China.     

Are they telling the truth? Revealing hidden traits of satisfaction with a public bike-sharing service

Public bike-sharing systems (BSSs) are an emerging mode of transportation introduced by municipalities to solve congestion problems in metropolitan areas, especially when integrated with other types of transportation. In the last years, the number of public bike-sharing services has been constantly on the rise all over the world, and generally the overall satisfaction with them is high. However, satisfaction with public services is driven by mechanisms that can differ from those in the private sector. It is important to establish to what extent a high satisfaction is genuine or simply ephemeral. Even “old” public services (like public transportation) become “gold” when accompanied by the introduction of new technologies. In this paper we analyze this phenomenon using data from a satisfaction web-survey conducted among customers of the public BSS “BikeMi” in Milan, Italy, in a period when mobile technologies have been introduced to speed up the service. On analyzing the responses to satisfaction questions using simple summary statistics, the level of satisfaction resulted very high. However, our aim was to look for potential “darker” sides of the service by detecting possible hidden satisfaction components. For this purpose, we used the Nonlinear Principal Components Analysis, which is particularly powerful in this context. A simple textual analysis was also performed as a validating test. Results from our analysis indicated that satisfaction is flawed by a set of factors like the mechanics of the bikes, the picking and dropping system, and the apps used to organize the service. Less concern was detected for more general aspects of the service.     

Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity

Traffic state in the urban network is a direct reflection of the operational efficiency of the urban transportation system. As the busiest period of the day, traffic states during evening peak hours can effectively measure the capacity and efficiency of the transportation system. The primary objective of this study is to investigate how the potential factors affect traffic states during evening peak hours on weekdays. The geographically weighted regression (GWR) approach was proposed to model the spatial heterogeneity of traffic states and visualize the spatial distributions of parameter estimations. Four types of data including traffic state index (TSI) data, point of interests (POIs) data, road features data, and public transport facilities data were obtained from Shanghai in China to illustrate the procedure. According to the results, the GWR model outperformed the ordinary least square (OLS) model in the explanatory accuracy as well as the goodness of fit. The urban form was revealed to have a significant influence on traffic states and strong local variability for parameter estimations was observed. The number of public and commercial POIs, residential POIs, bus routes, bus stops, the average number of lanes, as well as average traffic volumes can significantly affect the traffic states spatially, and the estimated coefficients of each traffic analysis zone (TAZ) vary across regions. The conclusions of this study may contribute to making the planning and management strategies more efficient for alleviating traffic congestion.     

A social equity lens on bus bridging and ride-hailing responses to unplanned subway disruptions

During subway disruptions, commuters are often left stranded while they wait for bus bridging services. Some are able to change their mode of transport midway through their trip, often by requesting a ride-hailing service like Uber or Lyft if they are affordable. Many agencies use in-service buses to provide bus bridging services during subway disruptions, leading to reduced levels of service for other bus riders. Bus bridging policies and the affordability of ride-hailing raise questions regarding the equitable distribution of transit capacity during subway disruptions. After analyzing 78 major subway disruptions in Toronto, we found that bus routes serving disadvantaged populations were more likely to be negatively impacted by diversions for subway disruptions, than routes operating in more affluent areas of the city. We also found that ride-hailing demand increased during subway disruptions, but not in the more disadvantaged neighbourhoods. The research points to inequalities in how transit riders are impacted by subway disruptions, and further identifies how ride-hailing actually serves to reinforce these inequalities, rather than ameliorate them.     

Understanding the usage of dockless bike sharing in Singapore

A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.     

Uber service area expansion in three major American cities

Chiefly led by Uber, on-demand ride-hailing services have transformed the urban American transportation landscape in merely the past decade. Utilizing the proliferation of internet-enabled smartphones, this app-based company has provided city inhabitants with a convenient and reliable door-to-door mobility service, which has arguably improved car-based accessibility while also generating a host of negative environmental and societal externalities. While to date the utilization of Uber has largely been an urban phenomenon, the lasting success of this new mobility option likely rests within its ability to expand its services into suburban communities. Yet, given the competitive nature of the ride-hailing marketplace and genuine concerns over passenger and driver anonymity, transportation planners and urban policymakers have been stymied in their ability to access the disaggregate data sets needed to help assess whether these services are in fact extending beyond city centers and identify which factors may be contributing to any expansion into more peripheral suburban neighborhoods. By introducing a creative strategy using the privacy-related suppression processes of Uber Movement data, this study quantifies the continued expansion of Uber's ride-hailing service into outlying communities from 2016 to 2018 by employing a multilevel modeling approach to recognize the neighborhood-level socioeconomic and built environment factors most related to this service expansion in three major American cities: Boston, San Francisco, and Washington, DC.     

Modeling bike-sharing demand using a regression model with spatially varying coefficients

As an emerging mobility service, bike-sharing has become increasingly popular around the world. A critical question in planning and designing bike-sharing services is to know how different factors, such as land-use and built environment, affect bike-sharing demand. Most research investigated this problem from a holistic view using regression models, where assume the factor coefficients are spatially homogeneous. However, ignoring the local spatial effects of different factors is not tally with facts. Therefore, we develop a regression model with spatially varying coefficients to investigate how land use, social-demographic, and transportation infrastructure affect the bike-sharing demand at different stations to address this problem. Unlike existing geographically weighted models, we define station-specific regression and use a graph structure to encourage nearby stations to have similar coefficients. Using the bike-sharing data from the BIXI service in Montreal, we showcase the spatially varying patterns in the regression coefficients and highlight more sensitive areas to the marginal change of a specific factor. The proposed model also exhibits superior out-of-sample prediction power compared with traditional machine learning models and geostatistical models.     

Unobserved heterogeneity in transportation equity analysis: Evidence from a bike-sharing system in southern Tampa

Assessing the equity impacts of transportation systems/policies has become a crucial component in transportation planning. Existing statistical modeling approaches for transportation equity analysis have typically assumed that parameter estimates are constant across all observations and used data aggregated to certain geographic units for the analysis. Such methods cannot capture unobserved factors that are not contained in the dataset, i.e., unobserved heterogeneity, which is likely to be present in the increasingly popular disaggregated datasets. To investigate whether there is unobserved heterogeneity in transportation equity impacts, this study carries out an empirical study focusing on the distribution of individual accessibility to activity locations via bike-sharing in southern Tampa. A disaggregated dataset containing information on individual bike-sharing accessibility and socio-economic factors is modeled with a random parameters logit model that allows for the investigation of possible unobserved heterogeneity. Further, models are estimated using data aggregated to parcel- and TAZ-levels to explore the impacts of data aggregation on model estimation results. The models unveil the unobserved heterogeneity in bike-sharing accessibility among populations in different groups defined by different sociodemographic factors in southern Tampa. These results shed insights into how the inconsistent disparity direction of transportation outcomes across individuals in a population group can be measured from the heterogeneity effects. Finally, a comparison between different models show that to capture such inconsistency, the use of disaggregated data with heterogeneity models is highly recommended for transportation equity analysis.     

Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China

In recent years, dockless bike-sharing has rapidly emerged in many cities all over the world, which provides a flexible tool for short-distance trips and interchange between different modes of transport. However, new problems have arisen with the fast and extensive development of the dockless bike-sharing system, such as high running expenses, ineffective bike repositioning, parking problems and so on. To improve the operations of the dockless bike-sharing system, this study aims to investigate the travel pattern and trip purpose of the bike-sharing users by combining bike-sharing data and points of interest (POIs). A massive amount of bike-sharing trips was obtained from the Mobike company, which is a bike-sharing operator in China. The POIs surrounding each trip origin and destination were derived from the Gaode Map application programming interface. K-means++ clustering was adopted to investigate dockless bike-sharing travel patterns and trip purpose based on trip records and their surrounding POIs. The clustering results show that on weekdays, bike-sharing trip origin and destination can be divided into five typical groups, i.e., dining, transportation, shopping, work and residential places. Dining is the most popular trip purpose by bike-sharing, followed by the transferring to other transportation modes and shopping. In addition, through understanding the spatial distribution of the bike-sharing usage patterns of five typical activities, strategies for improving the operation of the dockless bike-sharing system are provided.     

Not minding the gap: Does ride-hailing serve transit deserts?

Transit has long connected people to opportunities but access to transit varies greatly across space. In some cases, unevenly distributed transit supply creates gaps in service that impede travelers' abilities to cross space and access jobs or other opportunities. With the advent of ride-hailing services like Uber and Lyft, however, travelers now have a new potential to gain automobility without high car purchase costs and in the absence of reliable transit service. Research remains mixed on whether ride-hailing serves as a modal complement or substitute to transit or whether ride-hailing fills transit service needs gaps. This study measures transit supply in Chicago and compares it to ride-hailing origins and destinations to examine if ride-hailing fills existing transit service gaps. Findings reveal clustering of ride-hailing pickups and drop-offs across the City of Chicago, but that the number of ride-hailing pickups and drop-offs was most strongly associated with high neighborhood median household income rather than measures of transit supply. In bivariate analyses, transit service was not associated with ride-hailing trip ends. But after controlling for neighborhood socioeconomic status, transit dependency, population density, and employment density, we found fewer ride-hailing trips in neighborhoods where bus service dominated and significantly more ride-hailing trips where rail service was prevalent. Patterns were slightly different for overnight weekend ride-hailing pick-ups, where higher transit density predicted a greater number of trips in nearby tracts. Additional research and policy is needed to ensure that ride-hailing services provide travel options to those who need them the most and fill transit gaps in low-income communities when options to increase service are limited.     

Urban transport policy transfer: “bottom-up” and “top-down” perspectives

The paper provides insights into the urban transport policy transfer process, focusing particularly on the transfer of the transport policy within the EU. The themes of the paper are structured according to five of the “Dolowitz and Marsh questions”: what is transferred?; why do actors engage in policy transfer?; who are the key actors involved in the policy transfer process?; from where are the lessons drawn?; and what restricts or facilitates the policy transfer process? The methodological approach taken for considering each question involves two steps. Firstly, a “bottom-up” step considers the views of policy transfer from a “city perspective”, for which use is made of results from interviews recently carried out within the EU project “Transport Research Knowledge Centre” (TRKC). These interviews were intended to ascertain the information needs of seven “representatives” of European cities, all of whom were involved in the Cities Reference Group of the EU project “Citymobil”. These seven cities have widely varying characteristics in terms of size and geographical location (across Europe). By discussing information needs, the interviewees provided many insights into the transport policy transfer process. Secondly, a “top-down” step considers the policy transfer questions from an “EU perspective’; use here is made of various transport policy documents published by the European Commission (EC). For each of the five questions, “bottom-up” and “top-down” perspectives are examined and compared. The final section of the paper draws conclusions, providing a number of recommendations to both city authorities and the EU on how urban transport policy transfer might be enhanced in the future.     

Measuring when Uber behaves as a substitute or supplement to transit: An examination of travel-time differences in Toronto

Policymakers in cities worldwide are trying to determine how ride-hailing services affect the ridership of traditional forms of public transportation. The level of convenience and comfort that these services provide is bound to take riders away from transit, but by operating in areas, or at times, when transit is less frequent, they may also be filling a gap left vacant by transit operations. These contradictory effects reveal why we should not merely categorize all ride-hailing services as a substitute or supplement to transit, and demonstrate the need to examine ride-hailing trips individually.Using data from the 2016 Transportation Tomorrow Survey in Toronto, we investigate the differences in travel-times between observed ride-hailing trips and their fastest transit alternatives. Ordinary least squares and ordered logistic regressions are used to uncover the characteristics that influence travel-time differences. We find that ride-hailing trips contained within the City of Toronto, pursued during peak hours, or for shopping purposes, are more likely to have transit alternatives of similar duration. Also, we find differences in travel-time often to be caused by transfers and lengthy walk- and wait-times for transit. Our results further indicate that 31% of ride-hailing trips in our sample have transit alternatives of similar duration (≤15 minute difference). These are particularly damaging for transit agencies as they compete directly with services that fall within reasonable expectations of transit service levels. We also find that 27% of ride-hailing trips would take at least 30 minutes longer by transit, evidence for significant gap-filling opportunity of ride-hailing services. In light of these findings, we discuss recommendations for ride-hailing taxation structures.     

Characterizing the movement patterns of minibus taxis in Kampala's paratransit system

Urban travelers in Africa depend on minibus taxis for their daily social and business commuting. This paratransit system is loosely regulated, self-organizing, and evolves organically in response to demand. Our study used floating car data to analyze and describe the movement characteristics of nine minibus taxis in Kampala, Uganda. We made three intriguing findings. Firstly, in searching for, picking up and transporting passengers, minibus taxi trajectories followed a heavy-tailed power-law distribution similar to a “Lévy walk”. Secondly, their routes' topology and shape gradually changed. Thirdly, the extraordinary winding (expressed in terms of tortuosity) of the paths suggested the extreme determination of the drivers' search for passengers. Our findings could help city planners to build on the self-organizing characteristics of the minibus taxi system, and improve the mobility of travelers, by optimizing routes and the distribution of public amenities.     

Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago

As ride-hailing becomes more common in cities, public agencies increasingly seek transportation network company (TNC) service data to understand (and potentially regulate) demand and service response. Despite the increase in ride-hailing or TNC demand and subsequent research into its determinants, there remains little research on shared TNC trips and the spatial distribution of trip demand across demographic and land use variables. Using Chicago as a case study, shared TNC trip data from 2019 was used to estimate the count and ratio of shared ride services, based on built environment, demographic, location, time of day, and trip details. Findings reveal that trip length, day of week designation, density of pedestrian and multi-modal infrastructure, and underlying socioeconomic characteristics of the origin zones influence the proportion and count of shared ride-hail trips. Of concern is that those using transit or active modes may be taking more ride-hailing trips, but these Chicago-region results indicate that the provision of pedestrian infrastructure and remoteness to transit stops result in fewer shared trips.     

Inter-city connections in China: High-speed train vs. inter-city coach

High-speed train (HST) and inter-city coach (ICC) have been two important ground transportation modes for travelling between cities in China. They influence inter-city connections significantly. This study uses HST's and ICC's timetable data to construct networks; evaluates city centrality and city-pair connectivity to compare the hierarchical structures. The results show that the HST network shows linear distribution characteristics while ICC network presents regional “core-periphery” structure. Provincial administrative boundaries have an obvious constraint on the ICC network, while the HST community structure follows the railway lines' distribution. Finally, this study illustrates the spatial organization model and gives implications for regional transportation planning.