Informality and accessibility to jobs by public transit: Evidence from the São Paulo Metropolitan Region

Access to opportunities through public transport can have different impacts on individual's life especially in developing countries where opportunities are limited, job informality rates are high, and socioeconomic characteristics gaps are big. The aim of this paper is to explore the relationship between job informality and accessibility to employment by public transport in São Paulo Metropolitan Region (SPMR), Brazil. To do so, we calculate a cumulative-opportunity measure of accessibility to jobs for 633 areas within the SPMR. We use a multilevel mixed-effects logistic regression model to estimate the effect of job accessibility on the likelihood of being informally employed, controlling for individual and other area characteristics. To account for informal sector heterogeneity, two regression models are generated: one for the workers earning below minimum wage and one for the workers earning above minimum wage. The results show that accessibility to jobs is unevenly distributed across the region, largely concentrated in the core of the region, and especially in the high-income areas. The regression results show that for workers earning less than the minimum wage, a higher level of accessibility to jobs by public transport is associated with a lower likelihood of being a worker in the informal job sector. For informal workers earning more than the minimum wage, car ownership seem to be more relevant than transit accessibility in determining the likelihood of being part of the informal job sector. In light of these findings, increasing accessibility by public transport through either expanding transit services to areas with high informality rates to have a better access to formal jobs or supporting the decentralization of formal jobs may be a way to achieve reductions in informality rates, especially among those earning less than the minimum wage.     

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.     

Multi-city exploration of built environment and transit mode use: Comparison of Melbourne,Amsterdam and Boston

The built environment is an important determinant of travel demand and mode choice. Establishing the relationship between the built environment and transit use using direct models can help planners predict the impact of neighborhood-level changes, that are otherwise overlooked. However, limited research has compared the impacts of the built environment for different networks and for individual transit modes.This paper addresses this gap by developing built environment and transit use models for three multimodal networks, Amsterdam, Boston and Melbourne, using a consistent methodology. A sample of train, tram and bus sites with similar station-area built environments are selected and tested to establish if impacts differ by mode. It is the first study that develops neighborhood-level indicators for multiple locations using a consistent approach.This study compares results for ordinary least squares regression and two-stage least squares (2SLS) regression to examine the impact of transit supply endogeneity on results. Instrumented values are derived for bus and tram frequency in Melbourne and bus frequency in Boston. For other mode and city combinations, the 2SLS approach is less effective at removing endogeneity.Results confirm that different associations exist between the built environment and transit modes, after accounting for mode location bias, and that this is true in multiple networks. Local access and pedestrian connectivity are more important for bus use than other modes. Tram is related to commercial density. This finding is consistent for all samples. Land use mix and bicycle connectivity also tend to be associated with higher tram use. Train use is highest where opportunities exist to transfer with bus. Population density is commonly linked to ridership, but its significance varies by mode and network.More research is needed to understand the behavioral factors driving modal differences to help planners target interventions that result in optimal integration of land use with transit modes.     

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.     

Understanding the effects of complex seasonality on suburban daily transit ridership

Fluctuations in transit ridership pattern over the year have always concerned transport planners, operators and researchers. Predominantly, metrological elements have been specified to explain variability in ridership volume. However, the outcome of this research points to new direction to explain ridership fluctuation in Brisbane. It explored the relationship between daily bus ridership, seasonality and weather variables for a one-year period, 2012. Rather than segregating the entire year’s ridership into the four calendar seasons (summer, autumn, spring, and winter), this analysis distributed the yearly ridership into nine complex seasonality blocks. These represent calendar season, school/university (academic) period and their corresponding holidays, as well as other observant holidays such as Christmas. The dominance of complex seasonality over typical calendar season was established through analysis and using Multiple Linear Regression (MLR). This research identified a very strong association between complex seasonality and bus ridership. Furthermore, an expectation that Brisbane’s subtropical summer is unfavourable to transit usage was not supported by the findings of this study. A nil association of precipitation and temperature was observed in this region. Finally, this research developed a ridership estimation model, capable of predicting daily ridership within very limited error range. Following the application of this developed model, the estimated annual time series data of each suburb was analysed using Fourier Transformation to appreciate whether any cyclical effects remained, compared with the original data.     

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.     

Study design impacts on built environment and transit use research

The different factors examined in studies linking the built environment and transit use explain about half of the variability in findings for travel behavior. Despite many differences in the research design of these studies, it is not known if choices about study design impact theoretical consistency in results and account for some of the unexplained variance between studies. This gap exists because multiple studies must be analyzed together to explore the topic. This study aims to fill this gap, using a sample of data points and statistical models from 146 studies identified through a comprehensive database search.This paper first synthesizes the study design adopted in empirical studies of the built environment and transit use. Meta-regression is then used to identify study design aspects causing significant differences. Selective reporting bias appears to slightly exaggerate estimates for built environment Density and Accessibility. Over 40% of variability in findings for Density and Diversity was explained by study design aspects. These include whether collinearity of variables is accounted for, the specificity of the sample population and transit mode, catchment size; and the number of explanatory variables specified.Overall the average correlations for built environment and transit use are weak (<0.2). Predictions of transit ridership based on built environment factors are likely to be imprecise, so models should be carefully specified. Given the impact of study design, adherence to best practice conventions could reduce variance within studies and dispersion between studies. For ambiguous specification issues, sensitivity testing could be used to generate prediction intervals. Further investigation of factors such as transit mode and catchment size would be useful to determine if there is a theoretically plausible reason to favor certain specifications.     

Disability,wages, and commuting in New York

In the U.S., substantial employment and wage gaps persist between workers with and without disabilities. A lack of accessible transportation is often cited as a barrier to employment in higher wage jobs for people with disabilities, but little is known about the intraurban commuting patterns of employed people with disabilities in relation to their wage earnings. Our study compares wages and commute times between workers with and without disabilities in the New York metropolitan region and identifies the intraurban zones where residents experience higher inequities in wage earnings and commute times. We obtained our data from the Public Use Microdata Sample (PUMS) of the American Community Survey (ACS) for the 2008–2012 time period. We used linear mixed-effects models and generated separate models with log hourly wage or one-way commute time as the dependent variable. We find significant differences in wages and commute times between workers with and without disabilities at the scale of the metropolitan region as well as by intraurban zone. At the metropolitan scale, disabled workers earn 16.6% less and commute one minute longer on average than non-disabled workers. High commute and wage inequalities converge in the center, where workers with disabilities are more likely to use public transit, earn 17.1% less, and travel nearly four minutes longer on average than workers without disabilities. These results suggest that transport options are less accessible and slower for disabled workers than they are for non-disabled workers. Our findings indicate a need for more accessible and quicker forms of transportation in the center along with an increased availability of centrally located and affordable housing to reduce the disability gap in wages and commute times. We also find that workers with disabilities generally seek higher wages in exchange for longer commute times, but the results differ by race/ethnicity and gender. Compared to white men, minority workers earn much less, and white and Hispanic women have significantly shorter commute times. Our findings offer new geographic insights on how having a disability can influence wage earnings and commute times for workers in different intraurban zones in the New York metropolitan region.     

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.     

How does the propensity of living near rail transit moderate the influence of rail transit on transit trip frequency in Xi'an?

Many cities have made massive investments on rail systems to substitute transit for driving. Some studies have considered the confounding effect of attitudes in the connections between rail transit and travel behavior. However, they often focused on the average effect of rail transit and assumed that individuals' responses to transit improvements do not vary by their tastes. Using the 2014 data from Xi'an in China, this study explores the interaction effect between metro transit (heavy rail) and the propensity (i.e., predicted probability) of living in neighborhoods with metro transit on transit use. The propensity is positively associated with commute by metro transit and bus. Further, individuals with a strong propensity use transit equivalently no matter whether they live near metro transit, but metro transit tends to promote transit commute for those with a weak propensity of living near metro transit. Overall, building a rail line helps enhance transit ridership. Planners should also consider the variation in responses by individuals with different tastes when using policies to shape urban travel.     

An investigation of the performance of urban rail transit systems on the corridor level: A comparative analysis in the American west

Since the 1980s, significant investments have been made in urban rail transit across the United States, particularly using light rail technology. Most of these light rail systems have been built in Sunbelt cities which no longer had legacy rail systems. As a result, they were constructed using a building blocks approach, being funded corridor by corridor. Most research, however, on urban rail performance has taken place at the system-wide level, leaving a significant gap at the level of the transit corridor. This research examined nineteen urban rail corridors in Denver, Salt Lake City, and Portland. A performance score was constructed for each corridor based upon ridership per mile, ridership growth, capital costs, and the cost of ongoing operations. These scores were then compared with the geographic profile of each corridor studied. Corridors in each city ranked high and low, with no city emerging as a clear frontrunner. More centrally-located corridors in each city registered the highest performance scores, while longer corridors in more peripheral locations had lower performance scores. Headways, population density, job density, walkability, and percentage renter occupied housing units were found to have a statistically significant relationship with high corridor performance, largely in line with previous studies, though median income, bus connections, and park and ride spaces were not found to increase performance in this study.     

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.     

Exploring built environment correlates of walking distance of transit egress in the Twin Cities

Most studies on walking distance to transit stops either emphasize transit access or do not distinguish transit access and egress. Furthermore, environmental correlates of walking distance may differ by stop location. Using the 2010 Transit Onboard Survey in the Minneapolis and St. Paul Metropolitan Area, this study develops four models to compare the effects of the built environment around transit stops on walking distance of transit egress. Job density is negatively correlated with walking distance, consistent in all four models. Other built environment variables exhibit different impacts by stop location. Particularly, land use mix has positive impacts on walking distance for stops outside of downtown and suburban employment centers whereas job density is more important for suburban centers. Job accessibility and the number of intersections have significant effects on stops within downtown areas but have no significant impacts on stops outside of downtown areas. The number of transit stops has opposite impacts on walking distance for stops within and outside of downtown. Moreover, the built environment tends to have a larger impact on walking distance in downtown areas than non-downtown areas. We then discuss the implications for stop area land use planning and transit stop location choice.     

The relationship between recent gasoline price fluctuations and transit ridership in major US cities

The unprecedented increase in gasoline costs between August 2005 and July 2008 has become a major public issue in the US. Of the contentions and potential solutions surrounding higher gasoline costs, one receiving relatively little attention has been the role of public transit. This research examines that question by analyzing the relationship between gasoline prices and transit ridership from January 2002 to April 2008 in nine major US cities. Regression analysis is used to assess the degree to which variability in rail and bus transit ridership is attributable to gasoline costs and fluctuations in gasoline cost, controlling for service changes, seasonality, and inherent trending. The results indicate that a small but statistically significant amount of ridership fluctuation is due to changes in gasoline prices. The results are discussed in light of the policy and practical implications of higher gasoline prices for mass transit and the potential for long term changes in US travel behavior.     

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.     

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.     

Understanding bus rapid transit route ridership drivers: An empirical study of Australian BRT systems

Bus Rapid Transit (BRT) systems are an increasingly popular public transport option internationally. They provide rail-like quality for bus services for a fraction of the cost of fixed rail. Many claims of high and increasing ridership have resulted from BRT system development; however, it is unclear exactly which aspects of BRT system design drive this. This paper explores whether BRT design features, among other influences, significantly increase ridership above and beyond the impact of service levels. It does so using a series of regression models undertaken on 77 BRT and non-BRT bus routes in Australia which is known for its diversity in BRT route design. Explanatory variables used included service level, frequency, speed, stop spacing, share of segregated right of way, vehicle accessibility, employment and residential density, car ownership levels and BRT infrastructure quality. Five models explored the role of these variables. Two models found that service level dominates predictions of boardings per route km although they suffer from endogeneity. Further models control for this influence by modelling boardings per vehicle km. Overall results suggest that some BRT infrastructure treatments such as right of way have a significant impact on ridership but the influence of infrastructure is within the context of high service levels. The role of accessible vehicles has also been highlighted in this research, although more research is needed to clarify this influence. The paper concludes with a discussion of the various influences on ridership and recommendations for existing policy and future research.     

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.     

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.     

Performance analysis of rail transit investments in Turkey: İstanbul,Ankara, İzmir and Bursa

Rail transit investments require the highest amount of investment costs of all modes. Considering the high cost involved, it is particularly important that their performance justifies this high cost and that expectations from these investments are met. Therefore, in the world, it has become an important field of research to study the gap between the expectations from and outcomes of these investments in order to assess the performances.In Turkey, there is a growing interest in constructing rail transit systems in the cities. However, there has been a limited number of studies on the performance of these investments. It is not clear with what expectations these systems are built or whether these expectations are met. There seems to be an urgent need to study these rail investments, with a particular focus on their planning/investment objectives and outcomes.This paper compares the expectations with the actual outcomes. A sample group was selected among the cities currently operating rail transit systems: ?stanbul, Ankara, ?zmir and Bursa. Semi-structured interviews were made with the officers and planners that have involved in the planning or implementation phase of the systems. As the primary indicators of performance, cost and ridership forecast and outcome data are collected and considered in the comparison.It is found that systems performed rather poor in terms of expectations, such as attaining ridership forecasts, being built within budget etc. Hence there is a gap between expectations and outcomes.