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.     

Weather and rail delays: Analysis of metropolitan rail in Dublin

With changes in the global climate, the occurrence of severe weather events appears to be becoming ever more frequent. As a result of this, vital transport networks are becoming increasingly exposed to disruption or disablement due to weather related incidents. In order to adapt to these changing conditions it is important to gain an understanding of how weather currently impacts transport systems. This paper presents the results of a statistical analysis of the impact of weather conditions on the performance of metropolitan commuter rail based upon observations made on the Dublin Area Rapid Transit (DART) rail system. Utilising a dataset comprising daily performance observations for 30 train services operating across the DART network, this research applies a number of multiple regression models to gain an understanding of the role of weather, temporal effects, and resulting interactions, on delays experienced by the network. While research in this area has traditional focused on the impact of single events, this study presents an examination of the role of multiple factors and their interactions. With regard to temporal effects, the largest delays are observed in the last third of the year, with peak delays occurring in November. Delays due to adverse weather conditions are observed, with rain being the primary factor related to poor performance. Interactions between different weather conditions, particularly wind and rain, as well as between weather conditions and the month in which a journey took place were also observed to be significant and resulting in delays to services.     

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.     

Extreme weather,public transport ridership and moderating effect of bus stop shelters

This paper examines how extreme weather conditions influence urban public transport ridership with a particular focus on the role of bus stop shelters. Using bus ridership data from the Salt Lake City metropolitan area, we find that extreme weather such as very high and low temperatures, and heavy rainfall reduces public transport ridership, while bus stop shelters have a modest effect on mitigating ridership losses resulting from these adverse weather conditions. The moderating effect of shelters is more pronounced on weekdays, and for bus stops with lower service frequency and fewer transfers. Our research also shows that the installation of bus shelters correlates with a variety of factors including service frequency, land use types, and local socioeconomic and demographic characteristics. Overall, our findings suggest that public transport amenities with weather-proof attributes have the potential to retain and attract more ridership on extreme weather days.     

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.     

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.     

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.     

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.     

How does weather affect bikeshare use? A comparative analysis of forty cities across climate zones

This study examines the effect of weather on bikeshare use. We employ data from forty Public Bicycle Sharing Programs located in forty cities (16 countries) across five different climate zones, spanning tropical to boreal climates. Our curated dataset is longitudinal and consists of nearly 100 million cycling trips. Key findings include: (a) the most significant variable, particularly on weekdays, is the time of day, followed by precipitation; (b) in most cities, usage increases on weekdays and weekends up to a point around 27 to 28 °C, before declining; (c) usage by hour usually follows a bimodal or trimodal daily pattern on weekdays, except for schemes which are too small to serve a commuter function (weekend and weekday usage is similar in small schemes); (d) weekend usage peaks at around 2 to 3 pm in most schemes, except those in hotter climates where the peak is around 5 pm; (e) precipitation negatively affects female ridership more than male ridership; and, (f) a changing climate is likely to affect cycling by boosting ridership in cold climates and lowering ridership in warm climates, but the effects will likely be small. In the spirit of reproducibility, all data and R code are publicly available.     

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.     

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.     

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.     

Ridership estimation of a new LRT system: Direct demand model approach

The successful introduction of LRT systems is inevitably related to the realistic estimation of their ridership; this is particularly true for cases of no prior experience in the use of such modes in the part of the traveling public. This paper presents a practical approach for developing a direct demand model, for the case of a planned LRT system in Cyprus connecting the three major cities of Nicosia, Larnaca and Limassol. The proposed approach is based on existing traffic demand data and limited roadside surveys. Results indicate that the introduction of the proposed LRT would attract a moderate number of 23,000 passengers daily and shift a small percentage of 3.5% of traffic to the system. It was also found that approximately 33% of these trips correspond to the urban section of the network, while about 62% of the estimated ridership will use the part of the system connecting Nicosia and Larnaca.     

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.     

Examining changes in travel patterns among lower wealth households after BRT investment in Bogotá, Colombia

Bogotá, Colombia's TransMilenio Bus Rapid Transit (BRT) system has garnered praise for its beneficial effects on transit ridership, congestion, and air quality, yet there has been little research into the system's impacts on individuals and households, particularly on the city's lower wealth households. These households tend to be located in peripheral neighborhoods and access the BRT system largely via its sprawling network of feeder buses, rather than directly accessing the more central trunk lines. This paper examines the relationship between BRT access—especially feeder-based BRT access—and the degree to which the city's lower wealth households are able meet needs for mobility and out-of-home activity participation by analyzing changes in self-reported travel patterns among lower wealth households from before to after introduction of the BRT system. A secondary aim of this paper is to propose and test the variable ‘travel purpose diversity’ as an indicator of the degree to which households are able to meet their needs for out-of-home activity participation. Further work is needed to improve measurement of mobility in order to assess the household-level impacts of transit investments, particularly on lower wealth households. The results provide preliminary support for the validity of this indicator. Overall, findings suggest that the introduction of the BRT has not had a substantial or significant impact on the ability of Bogotá's lower wealth households to meet daily mobility needs. The paper presents some possible interpretations of these findings and offers suggestions for additional research to help improve our understanding of the impacts of Bogotá's transit investment.     

Urban rail transit PPPs: Survey and risk assessment of recent strategies

The proliferation of urban rail transit public–private partnerships (PPPs) in Latin America and Southeast Asia is a recent phenomenon. This paper first reviews the theoretical literature on the public procurement versus PPP decision in the context of rail transit and assesses the risks involved in entering these partnerships. The urban rail transit PPP approaches adopted in Bangkok, Kuala Lumpur, Buenos Aires, Rio de Janeiro, Singapore, Hong Kong, and London are described and classified into four broad approaches: (i) the development of new systems through Design-Build-Finance-Operate (DBFO), (ii) the concessioning of rail and subway services, (iii) the sale of state-owned operators through share issue privatization, and (iv) PPPs for infrastructure maintenance and upgrading. The different risk allocation strategies adopted under each of the above approach are analyzed for the extent to which they correspond to the theoretical recommendations. The concluding section provides a summary of the trade-offs that policy makers make when deciding between alternative strategies.     

Optimal allocation of protective resources in urban rail transit networks against intentional attacks

This paper advances the field of network interdiction analysis by introducing an application to the urban rail transit network, deploying protective resources against intentional attacks. The resource allocation problem for urban rail transit systems is considered as a game between two players, the attacker interdicting certain rail stations to generate greatest disruption impact and the system defender fortifying the network to maximize the system’s robustness to external interdictions. This paper introduces a game-theoretic approach for enhancing urban transit networks’ robustness to intentional disruptions via optimally allocating protection resources. A tri-level defender–attacker–user game-theoretic model is developed to allocate protective resources among rail stations in the rail transit network. This paper is distinguished with previous studies in that more sophisticated interdiction behaviors by the attacker, such as coordinated attack on multiple locations and various attacking intensities, are specifically considered. Besides, a more complex multi-commodity network flow model is employed to model the commuter travel pattern in the degraded rail network after interdiction. An effective nested variable neighborhood search method is devised to obtain the solution to the game in an efficient manner. A case study based on the Singapore rail transit system and actual travel demand data is finally carried out to assess the protective resources’ effectiveness against intentional attacks.     

Influence of the built environment on E-scooter sharing ridership: A tale of five cities

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