Land use effects on bicycle ridership: a framework for state planning agencies

State and local agencies increasingly recognize the importance of bicycling activity and as the number of riders has grown over the past several years, the agencies are becoming more aware of the need to provide better bicycle infrastructure. This paper proposes a series of empirical models and applies them to the State of Maryland in the United States, using a spatial lag approach to explore land use, built environment, demographic, socio-economic, and traffic condition connections to bicycle ridership, defined as the number of bicycle trips generated by a given analysis zone per day. A set of models is proposed for three land-use typologies: urban, sub-urban and rural. The data that drives this analysis was obtained from a recently conducted Household Travel Survey (HTS) in the Baltimore–Washington region in Maryland. Results show that some land uses, socio-economic and demographic characteristics, and transit accessibility are positively correlated with bicycle ridership. Other types of land use, transport system characteristics and income level have an inverse relationship with bicycle ridership. The contributing factors to bicycle ridership vary with land-use typology. This proposed approach could be used to evaluate factors relevant to bicycle demand. State and local agencies are advised to build designated bicycle paths according to traffic conditions and increase bicycle-parking capacity in specific establishments.     

Identification of land-use characteristics using bicycle sharing data: A deep learning approach

Extensive research has shown that urban land-use characteristics, including resident, work, consumption, transit, etc., are significantly interrelated with travel behaviors and travel demands. Many research efforts have been made to evaluate the impact of land use planning or policies on travel behavior, however, few studies are able to quantitatively measure the land-use characteristics based on the data of travel behaviors or travel demand. In this paper, a new hybrid model that combines time series feature extraction and deep neural network is proposed to identify regional land use characteristics and quantify land use intensity using ridership data of bicycle sharing. This method consists of four main parts: (i) A set of land-use characteristic labels are evaluated based on planning and Geographic Information System (GIS) data. (ii) An ensemble clustering method is used to determine the segmentation points of ridership time series. (iii) The statistical characteristics of the segmented time series are extracted and used as input to the neural network. (iv) A deep neural network is established and trained based on the processed ridership features and land-use labels. In terms of data collection, ridership data of the bicycle-sharing parking spots and land-use planning data are obtained from bicycle-sharing system and planning department in San Francisco Bay Area, California U.S.A., respectively. The test results show that this approach has high accuracy for identifying land-use characteristics based on several standard evaluation measures and that the identification distribution can be well explained. The extension results further prove that the model can be applied to effectively analyze the main land-use characteristics of the region although the identification results may become unstable after 3–4 months.     

The demand for reliable transit service: New evidence using stop level data from the Los Angeles Metro bus system

This study explores the role of service reliability in determining bus transit ridership. Using stop level service supply, demand, and performance data from the Los Angeles Metro bus system, I investigate whether reliability of a directional line serving a stop influences the number of passengers boarding the line at that stop, controlling for various other established factors affecting demand. This cross-sectional analysis of the variation in line boardings across about 1300 sample schedule time point bus stops served by about 300 directional bus lines over a six-month period uses a historical archive of real-time geo-referenced vehicle location data, and focuses on five different time periods, peaks and off-peaks, of a typical weekday. By evaluating two measures that capture different dimensions of bus service reliability, and by estimating a series of regression models, I find systematic evidence that higher average service punctuality (or schedule adherence) and lower variation in schedule deviation over time are associated with greater ridership, all else equal, particularly during the peak periods. This study also provides first empirical evidence that the effect of reliability on peak-period ridership is moderated by headway. The demand for reliability seems to be higher for lines with relatively longer headways. The findings indicate that service reliability influences transit mode choice and/or line/route selection, and suggest that system-wide ridership gains can be expected from reliability improvements. From an urban planning perspective, this study provides more evidence that good service quality can effectively compliment transformations in the urban fabric brought about by coordinated land use — transit plans to promote transit use.     

Walk-to-transit demand estimation methods applied at the parcel level to improve pedestrian infrastructure investment

Public transportation is a critical component of cities' transportation system that can be supported by a safe, complete, and connected pedestrian infrastructure. Agencies spend millions of dollars each year to improve transit ridership, yet many of the transit destinations do not have adequate pedestrian infrastructure to connect to the transit stops creating a substantial barrier to growing demand. This is particularly true in suburban areas. This paper presents a replicable methodology for estimating relative parcel-level transit demand such that analysts can conduct fine-grained evaluation and prioritization of the pedestrian network enhancements as they relate to public transit system. To this end, pedestrian infrastructure can boost transit ridership and enhance riders' safety. We rely on spatial data available in most cities coupled with land use and socioeconomic data to generate potential relative number of walk-to-transit trips for each parcel and weight the occupied road segments based on the results from mode choice and gravity models. Using this GIS-based tool, we identify road segments that have a higher potential in serving as a walking path to transit stops and prioritize gaps in existing sidewalk infrastructure. This result eliminates arbitrary sidewalk investment scoring programs and the reliance on transit walksheds to direct investment. We apply this method to a case study of the city of Knoxville and discuss the challenges and possible solutions. This approach can help city planners and engineers in data-oriented investment strategic management of sidewalk enhancement programs that support transit.     

Analysis on bike-share ridership for origin-destination pairs: Effects of public transit route characteristics and land-use patterns

Studies on bike-share programs have dramatically increased during the past decades. While numerous studies have examined various factors affecting bike-share demand at the station-level, few attempts have been made to understand bike-share ridership at the origin-destination (OD) level due to technical difficulties. The objective of this study is to examine whether existing public transit characteristics affect bike-share ridership at OD-level. We combined three datasets: (1) bike-share ridership data, (2) land-use and bike-transit infrastructure, and (3) bike-transit route characteristics between OD pairs of bike stations. Zero-inflated negative binomial (ZINB) regression models were used for the analysis. Our results showed that the travel distance between OD bike stations, land-use compositions, and the existence of bike-friendly infrastructures were significant factors determining bike-share ridership at the OD-level. In particular, a longer duration of public transit trips than bike-share, and more transit transfers, were associated with bike-share ridership. Further, this study showed that bike-share and public transit might compete with or promote each other, even within the city. The study's findings suggest that the relative efficiency of bike-share compared to public transit is highly associated with bike-share demand and help to increase the utility of bike-share system in response to several limitations of existing public transit networks.     

Addressing transit mode location bias in built environment-transit mode use research

Many studies have identified links between the built environment (BE) and transit use. However, little is known about whether the BE predictors of bus, train, tram and other transit modes are different. Studies to date typically analyze modes in combination; or analyze one mode at a time. A major barrier to comparing BE impacts on modes is the difference in the types of locations that tend to be serviced by each mode. A method is needed to account for this ‘mode location bias’ in order to draw robust comparison of the predictors of each mode.This study addresses this gap using data from Melbourne, Australia where three types of public transport modes (train, tram, bus) operate in tandem. Two approaches are applied to mitigate mode location bias: a) Co-located sampling – estimating ridership of different modes that are located in the same place; and b) Stratified BE sampling – observations are sampled from subcategories with similar BE characteristics.Regression analyses using both methods show that the BE variables impacting ridership vary by mode. Results from both samples suggest there are two common BE factors between tram and train, and between tram and bus; and three common BE factors between train and bus. The remaining BE predictors – three for train and tram and one for bus - are unique to each mode. The study's design makes it possible to confirm this finding is valid irrespective of the type of locations serviced by modes. This suggests planning and forecasting should consider the specific associations of different modes to their surrounding land use to accurately predict and match transit supply and demand. The Stratified sampling approach is recommended for treating location bias in future mode comparison, because it explains more ridership variability and offers a transferrable approach to generating representative samples.     

Transit ridership estimation with network Kriging: a case study of Second Avenue Subway,NYC

An attractive topic in transportation practice is transit ridership estimation. Reliable estimates are beneficial to spatial structuring, facility design, and vehicle operation, as well as financial and labor management. Traditional ridership estimation approaches mainly rely on regression models that consider subway fares, population, and employment distribution in surrounding areas. Yet consideration of ridership’s spatial dependency is largely lacking in these models. This paper recognizes the spatial effect by estimating the ridership of the new Second Avenue Subway in New York City using a network Kriging method. Network distance, instead of Euclidean distance, is used to reflect the fact that subway stations are only connected by subway tunnels. Results show that the new service should effectively relieve the traffic burden on other currently crowded subway lines.     

Travelling fair: Targeting equitable transit by understanding job location,sectorial concentration,and transit use among low-wage workers

Low-wage workers have a pressing need for adequate and affordable transportation services. However, the growing polycentricity of North American metropolises means transit providers face the difficult task of serving ever more dispersed employment centers. Deciding where limited project resources would provide the most benefit for disadvantaged populations should be a concern for transit planners and elected officials. The purpose of this research is to determine where low-wage employment zones are, where different types of low-wage jobs concentrate, and determine if job type and location have an effect on transit ridership for low-wage workers. We use a previously proposed method to identify low-wage employment zones in the Greater Toronto Hamilton Area, Canada and measure job type concentration using a gravity approach. We then test to see if job type concentration and employment centres relate to ridership, while controlling for other factors that influence mode share. Our results indicate significant differences in transit use for different occupations exist. These results can help guide more transit investment and research by tackling specific occupation's travel needs.     

Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership

In this study, we employ spatial regression analysis to empirically investigate the impacts of land use, rail service coverage, and rail station accessibility on rail transit ridership in the city of Seoul and the surrounding metropolitan region. Our analyses suggest that a rail transit service coverage boundary of 500 m provides the best fit for estimating rail transit ridership levels. With regard to land use, our results confirm that density is positively related to rail transit ridership within a 750 m radius of each station. In contrast, land use diversity is not associated with rail transit ridership. We also found that station-level accessibility is as important as land use for explaining rail transit ridership levels. Finally, we conclude that development density and station-level accessibility measures such as the number of station entrances or exits and the number of bus routes at the station are the most important and consistent factors for promoting rail transit ridership.     

Integrating node-place and trip end models to explore drivers of rail ridership in Flanders,Belgium

The node-place model is an analytical framework that was devised to identify spatial development opportunities for railway stations and their surroundings at the regional scale. Today, the model is predominantly invoked and applied in the context of ‘transit-oriented development’ planning debates. As a corollary, these model applications share the pursuit of supporting a transition towards increased rail ridership (and walking and cycling), and therefore assumingly a transition to more sustainable travel behavior. Surprisingly, analyses of the importance of node and place interventions in explaining rail ridership remain thin on the ground. Against this backdrop, this paper aims to integrate the node-place model approach with current insights that derive from the trip end modeling literature. To this end, we apply a series of regression analyses in order to appraise the most important explanatory factors that impact rail ridership in Flanders, Belgium, today. This appraisal is based on both geographical and temporal data segmentations, in order to test for different types of railway stations and for different periods of the day. Additionally, we explore spatial nonstationarity by calibrating geographically weighted regression models, and this for different time windows. The models developed should allow policy and planning professionals to investigate the possible demand impacts of changes to existing stations and the walkable area surrounding them.     

Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal

We studied transit ridership from the perspective of the transit provider, with the objective of quantifying the influence of transit system operational attributes, transportation system infrastructure attributes and built environment attributes on the disaggregate stop level boardings and alightings by time of day for the bus transit system in the Montreal region. A Composite Marginal Likelihood (CML) based ordered response probit (ORP) model, that simultaneously allows us to incorporate the influence of exogenous variables and potential correlations between boardings and alightings across multiple time periods of the day is employed. Our results indicate that headway affects ridership negatively, while the presence of public transportation around the stop has a positive and significant effect. Moreover, parks, commercial enterprises, and residential area, amongst others, have various effects across the day on boardings and alightings at bus stops. An elasticity analysis provides useful insights. Specifically, we observe that the most effective way to increase ridership is to increase public transport service and accessibility, whereas enhancements to land use have a smaller effect on ridership. The framework from our analysis provides transit agencies a mechanism to study the influence of transit accessibility, transit connectivity, transit schedule alterations (to increase/reduce headway), and land-use pattern changes on ridership.     

Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model

With the advance of intelligent transportation systems (ITSs) and data acquisition systems (DASs), it becomes possible in recent to explore the determinants of urban taxi ridership using multi-source heterogeneous data. This paper aims to use floating car data, points-of-interests (POIs) data and housing-price data to assess the influence of the built environment on taxi ridership. Within a scale of 0.5 km grid, critical indicators related to the economic aspect, intermodal connection, and land use factors were obtained using the multi-source data in Shanghai. To capture the spatial and temporal heterogeneity, Semi-parametric Geographically Weighted Poisson Regression (SGWPR) models are built over different time dimensions. It is found that SGWPR models result in higher goodness-of-fit than the generalized linear models. More importantly, the results show the impacts of built environment factors on taxi demand are highly heterogeneous, positive or negative in different city areas, reflected in the significant temporal variations of the effects. Overall, these findings suggest that the built environment factors have significant impacts on urban taxi demand, and the spatial context should not be ignored. Findings in this paper are expected to help better understand the relationship between urban taxi demand and built environment factors, improving the service level of the urban taxi system, and offering valuable insights into future urban and transportation planning.     

Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou,China

Understanding the relationship between the rail transit ridership and the built environment is crucial to promoting transit-oriented development and sustainable urban growth. Geographically weighted regression (GWR) models have previously been employed to reveal the spatial differences in such relationships at the station level. However, few studies characterized the built environment at a fine scale and associated them with rail transit usage. Moreover, none of the existing studies attempted to categorize the stations for policy-making considering varying impacts of the built environment. In this study, taking Guangzhou as an example, we integrated multi-source spatial big data, such as high spatial resolution remote sensing images, points of interest (POIs), social media and building footprint data to precisely quantify the characteristics of the built environment. This was combined with a GWR model to understand how the impacts of the fine-scale built environment factors on the rail transit ridership vary across the study region. The k-means clustering method was employed to identify distinct station groups based on the coefficients of the GWR model at the local stations. Policy zoning was proposed based on the results and differentiated planning guidance was suggested for different zones. These recommendations are expected to help increase rail transit usage, inform rail transit planning (to relieve the traffic burden on currently crowed lines), and re-allocate industrial and living facilities to reduce the commute for the residents. The policy and planning implications are crucial for the coordinated development of the rail transit system and land use.     

Mapping ridership using crowdsourced cycling data

Cycling volumes are necessary to understand what influences ridership and are essential for safety studies. Traditional methods of data collection are expensive, time consuming, and lack spatial and temporal detail. New sources have emerged as a result of crowdsourced data from fitness apps, allowing cyclists to track routes using GPS enabled cell phones. Our goal is to determine if crowdsourced data from fitness apps data can be used to quantify and map the spatial and temporal variation of ridership. Using data provided by Strava.com, we quantify how well crowdsourced fitness app data represent ridership through comparison with manual cycling counts in Victoria, British Columbia. Comparisons are made for hourly, AM and PM peak, and peak period totals that are separated by season. Using Geographic Information Systems (GIS) and a Generalized Linear Model we modelled the relationships between crowdsourced data from Strava and manual counts and predicted categories of ridership into low, medium, and high for all roadways in Victoria. Our results indicate a linear association (r² 0.40 to 0.58) between crowdsourced data volumes and manual counts, with one crowdsourced data cyclist representing 51 riders. Categorical cycling volumes were predicted and mapped using data on slope, traffic speeds, on street parking, time of year, and crowdsourced ridership with a predictive accuracy of 62%. Crowdsourced fitness data are a biased sample of ridership, however, in urban areas the high temporal and spatial resolution of data can predict categories of ridership and map spatial variation. Crowdsourced fitness apps offer a new source of data for transportation planning and can increase the spatial and temporal resolution of official count programs.     

Catchment if you can: The effect of station entrance and exit locations on accessibility

The success of passenger railway systems depends on their ridership and thus the population they serve. A mechanism to increase ridership is to expose the existing system to more people by reconfiguring the station itself, for instance by adding extra entrance and exit gates to shorten the walking distance from a trip's origin or its final destination. Gates are key nodes giving pedestrians access from street network to boarding/alighting facilities and vice versa. Stations and platforms are places not points, passengers may spend up to 6 min a trip walking between platforms and the end of the station nearest their origin or destination. This study systematically evaluates the accessibility of train stations and the effect of constructing an additional ‘far-side’ gate at stations with a single ‘near-side’ entrance. A three-step approach is defined to generate an isochrone as the catchment area for any transport node. Results indicate that stations with a single gate along their platforms (usually on one end of them) have the potential to increase the accessibility to jobs and population by around 10% on average. Due to the walking network and land use characteristics, some stations will benefit more significantly by retrofitting a new gate. Also, four linear regression models are developed to illustrate the effect of expanded accessibility on the number of entries and exits at each station for two peak periods. Then, stations are ranked based on their added ridership, which can help authorities to prioritize the development and allocating resources.     

A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting

Air transport demand forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. Total passengers are used as a proxy for air transport demand. However, the air passenger time series usually has a complex behavior due to their irregularity, high volatility and seasonality. This paper proposes a new hybrid approach, combining singular spectrum analysis (SSA), adaptive-network-based fuzzy inference system (ANFIS) and improved particle swarm optimization (IPSO), for short-term air passenger traffic prediction. The SSA is used for identifying and extracting the trend and seasonality of air transport demand and the artificial intelligence technologies, including ANFIS and IPSO, are utilized to deal with the irregularity and volatility of the demand. The HK air passenger data are collected to establish and validate the forecasting model. Empirical results clearly points to the enormous potential that the proposed approach possesses in air transport demand forecasting and can be considered as a viable alternative.     

Measuring the vulnerability of public transport networks

In the last years, studies on the vulnerability of public transport networks attract a growing attention because of the repercussions that incidents can have on the day-to-day functioning of a city. The aim of this paper is to develop a methodology for measuring public transport network vulnerability taking the Madrid Metro system as an example. The consequences of a disruptions of riding times or the number of missed trips are analysed for each of the network links with a full scan approach implemented in GIS (Geographic Information Systems). Using real trips distribution, each link in the network is measured for criticality, from which the vulnerability of lines and stations can be calculated. The proposed methodology also makes it possible to analyse the role of circular lines in network vulnerability and to obtain a worst-case scenario for the successive disruption of links by simulating a targeted attack on the network. Results show the presence of critical links in the southern part of the network, where line density is low and ridership high. They also highlight the importance of the circular line as an element of network robustness.     

Modelling and analysing effects of complex seasonality and weather on an area's daily transit ridership rate

Adverse weather is generally perceived as deterrent for public transit uses. This has also been highlighted in previous literatures. In contrary, our previous study found no association between weather and transit ridership while investigating the underlying temporal influences behind variation in daily ridership across the sub-tropical city of Brisbane, Australia. This contraindication led to the primary focus of this research. This research acknowledged that Inclusion of weather variables in conjunction with other relatively strong independent variables might result in washout of the weather effects on ridership. Variables such as rainfall do not recur on a daily basis throughout the year. Thus, generalising their effect on ridership with other independent variables that consistently influence ridership may create a similar problem. Hence, weather variables were converted into their normalised factors and combined with other independent variables while formulated the optimised the daily ridership rate estimation model. Several models were developed concerning various combinations of weather variables and through rigorous analysis it was identified that only the rain variable has noticeable effect on daily ridership. Evidently, this study functions as an update of our former study by directing towards a new approach to the analysis of the relationship between weather and transit ridership.     

Selecting tram links for priority treatments - The Lorenz Curve approach

Road and public transport authorities often have a difficult task in deciding which road links to select for investment in preferential traffic and public transport measures to improve public transport service performance. This paper presents a new approach which adopts the economic concept of the Lorenz Curve to compare link performance in terms of transit operations as well as weighted passenger volume of travel. The paper explores if, and how, these metrics can be re-interpreted to help with targeting improvements for on-road public transport and priority mitigations. The approach collates operational performance data, in this case link speed and also link public transport travel volume to plot the cumulative distribution of link speed/ridership performance as a curve. Two sets of test applications are presented; on a route level basis and secondly a network level analysis. The network level results present the most powerful results with the Lorenz Curve analysis able to quickly identify links that justify greater attention for preferential treatments since they have the worst 20th percentile of operational performance but the highest 40th percentile of relative link ridership. Mapping shows the problem links to be busy routes leading into the central city. Implications for wider application of these methods are discussed.     

Transit market research using structural equation modeling and attitudinal market segmentation

This paper presents a comprehensive approach for identifying potential transit markets and for developing strategies to increase public transport ridership. The approach uses structural equation modeling (SEM) to identify simultaneously travelers’ attitudes, travel behavior, and the causal relationships between a traveler's socioeconomic profile and his/her attitude toward travel. Travel attitudes are also used to identify distinct market segments and to develop plans that best serve the needs of each segment and increase transit ridership. The approach is demonstrated with a case study from the Utah Transit Authority.