Public Transportation Agencies Research Articles

Estimation of the effect on air quality of retrofitting SCRT on urban buses in Copenhagen

ABSTRACT This study estimates the effect on air quality of retrofitting SCRT on about 300 urban buses in Copenhagen from September 2015 to March 2016. The retrofitted buses were of Euro III, Euro IV and EEV emission standards. The specific SCRT technology applies ammonia as injected into the exhaust as a gas as opposed to normally as a liquid (urea). This technology is more efficient in reducing NOx emissions, especially under urban driving conditions with relatively low exhaust temperatures. The estimation of the effect is based on air quality model calculations for 98 selected busy streets in Copenhagen for 2015 based on, among others, information from the Zealand public transport agency about buses with and without retrofitted SCRT. More detailed analyses were conducted for two of the streets where fixed air quality measuring stations are located in Copenhagen. Furthermore, a before-after analysis of the development of measured concentrations at fixed measuring stations was carried out to isolate the effect of the retrofitted SCRT. The model calculations showed substantial reductions in emissions of NOx and exhaust particles from each bus (90%) but low reductions in concentrations of NO2, PM2.5 and PM10, respectively 3% for 98 streets on average for NO2, and 0.1%[0.2%] for PM2.5 and 0.07%[0.1%] for PM10 for H.C. Andersen Boulevard and [Jagtvej]. Based on the analysis of trends in the measurements it was not possible to isolate an effect of SCRT on urban buses in Copenhagen probably due to the large variations in meteorology affecting the variations in concentrations.

Particle swarm optimization and RBF neural networks for public transport arrival time prediction using GTFS data

Accurate prediction of Public Transport (PT) mobility is important for intelligent transportation. Nowadays, mobility data have become increasingly available with the General Transit Feed Specification (GTFS) being the format for PT agencies to disseminate such data. Estimated Time of Arrival (ETA) of PT is crucial for the public, as well as the PT agency for logistics, route-optimization, maintenance, etc. However, prediction of PT-ETA is a challenging task, due to the complex and non-stationary urban traffic. This work introduces a novel data-driven approach for predicting PT-ETA based on RBF neural networks, using a modified version of the successful PSO-NSFM algorithm for training. Additionally, a novel pre-processing pipeline (CR-GTFS) is designed for cleansing and reconstructing the GTFS data. The combination of PSO-NSFM and CR-GTFS introduces a complete framework for predicting PT-ETA accurately with real-world data feeds. Experiments on GTFS data verify the proposed approach, outperforming state-of-the-art in prediction accuracy and computational times.

Transit Stigma: Transit Administrators’ Views of the Role of Policy Elites in Perpetuating Stigma

Public transportation use is heavily stigmatized in the United States. As a result, those who depend on public transportation services for mobility are marginalized and subject to unjust social assumptions about their character, work ethic, and overall place in society. Prior research documents that public transit administrators are actively working to combat transit stigma in their service area; however, questions remain regarding how public transit administrators view the role of policy elites (elected or appointed political figures) in perpetuating transit stigma. Through a series of in-depth interviews with 20 transit administrators from across the United States, this research finds that policy elites maintain transit stigma through both formal and informal roles by devaluing the role of transit in society, focusing on short-term political goals, and restricting funding opportunities for public transportation agencies.

Whose express access? Assessing the equity implications of bus express routes in Montreal, Canada

Express buses—characterized by limited stops and sometimes higher frequencies or priority traffic measures—offer a cost-effective and efficient way to boost service convenience and reliability for riders. This paper assesses how the accessibility benefits of express bus route policy are distributed in Montreal, Canada, while providing a pathway for public transportation agencies to assess their policies and plans. To isolate the impact of bus express routes, we use General Transit Speed Specification (GTFS) data, the Open Trip Planner multimodal routing engine, and the 2013 edition of Montreal’s origin-destination survey to contrast travel time and accessibility at the trip and census-tract levels under two scenarios: one with the existing, complete network and the second a counterfactual scenario with no express bus routes. Our results indicate that bus express routes enable an overall increase in accessibility for the overall population. However, the accessibility benefits do not accrue evenly, as expected, but also tend to benefit a more significant number of higher incomes. This occurs despite the location of low-income populations in some outlying areas of the city, which express bus routes are supposed to serve. This paper closes with policy recommendations that help planners balance economic, environmental, and equity goals, perhaps one of the most complex challenges they face nowadays.

All ridership is local: Accessibility, competition, and stop-level determinants of daily bus boardings in Portland, Oregon

Research on accessibility, a measure of ease of reaching potential opportunities, has advanced significantly, but the adoption of these measures by public transport agencies has lagged. One explanation may be that research has been conducted at different spatial scales from the stop level typically used by agencies. To address this gap, this study examines the relationship between accessibility to jobs and average daily bus boardings at the bus-stop level of analysis in Portland, Oregon. Our models show that daily boardings could increase by 1.8% to 2.1% for every 10% increase in accessibility, measured as the number of jobs reachable in 30 min from the bus stop by public transport. This finding supports the argument that accessibility-focused service improvements have the potential to bolster stop-level ridership since network adjustments and new services like bus-rapid-transit often yield considerable increases in accessibility. At the same time, inter-stop competition reduces an individual stop's ridership. This study conveys the benefits of planning for accessibility at a regional scale and links regional decisions back to stop-level ridership, the context most familiar to public transport agencies, in the hope that this will accelerate and extend the adoption of accessibility in practice.

Efficient capacity allocation on deregulated railway markets

As railway markets are increasingly deregulated, coordinating and prioritising between capacity requests becomes more complex and more important. This paper describes the advantages and challenges of different allocation methods in vertically separated open-access railway markets, with several railway operators and heterogeneous traffic, and where a public infrastructure manager must resolve operators’ conflicting path requests. Three broad groups of allocation methods are described: administrative methods, allocation by social cost-benefit analysis and willingness-to-pay based allocation. We describe pros and cons of these allocation principles for three different market segments: commercial traffic with long planning horizons, (subsidized) traffic controlled by public transport agencies, and traffic with short planning horizons. We then outline an allocation process that meets the requirements of a deregulated market better than conventional methods. It is a mixed method, which uses an auction-like mechanism to allocate pre-defined paths to commercial operators on specified, capacity-constrained lines. The net social benefits of capacity reserved for traffic controlled by public transport agencies is assessed through social cost-benefit analysis of timetables. The benefits decrease when timetables are adjusted to make room for additional commercial train paths; the size of this loss determines the reservation price of the auction-like mechanism. Dynamic pricing is used for short-term allocation on congested line segments.

Analysis and prediction of ridership impacts during planned public transport disruptions

Urban metro and tram networks are regularly subject to planned disruptions, including closures, resulting from the need to maintain and renew infrastructure. In this study, we first empirically analyse the passenger demand response to planned public transport disruptions based on individual passenger travel behaviour, based on which we infer generalised journey time and cost elasticities for different passenger groups and time periods of the day. Second, we develop a model which enables predicting public transport demand for individual origin-destination pairs affected by a closure. The model is trained based on the empirically observed travel behaviour. The proposed method is applied to a case study closure in Amsterdam, the Netherlands, based on which we empirically derive generalised journey time and generalised journey cost elasticities of − 0.99 and − 1.11, respectively. Our results suggest that passengers’ demand response is lower for frequent users of the public transport network, as well as during weekdays - especially during the peak periods. Arguably, this stems from a higher share of captive passengers with a mandatory journey purpose in these segments, who will continue making their journey nevertheless. During weekends - with typically higher shares of leisure related journeys - a much more pronounced demand response is found. The estimated neural network regression model is able to predict passenger demand during public transport closures with a high level of accuracy. This provides public transport agencies more precise insights into the impact of closures on their revenue losses and on the potential need for resources reallocation.

Analysis of Route Choice During Planned and Unplanned Road Closures

The Federal Highway Administration (FHWA) Alternate Route Handbook proposes guidance to identify alternate routes during planned and unplanned road closures. A challenge with this process is the lack of traffic data available to decision-makers. High volume corridors experiencing unplanned closures can provide a rich case history by systematically collecting connected vehicle (CV) data during such incidents. CV data provide the ability to directly measure actual diversion routes and travel times during an ongoing or historical incident. This paper presents methodologies to systematically analyze diversion data to identify the most common alternate route choices and impacted interstate exits, valuable information for public safety and transportation agencies to evaluate the surrounding road network’s resiliency in accommodating diverting traffic. Agencies can use this information to proactively deploy resources (officers, signs, barricades) at critical locations during future closures. The scalability of this methodology is demonstrated by evaluating 12 additional cases to assess diversion rates found to be in the range of 58% to 93% for total closures exceeding five hours. The paper concludes by recommending agencies apply these methodologies to develop data-driven diversion strategies on critical routes coupled with real-time CV monitoring in dispatch centers to provide agile adjustment of resources along diversion routes.

Are commuter train timetables consistent with passengers’ valuations of waiting times and in-vehicle crowding?

Social cost-benefit analysis is often used to analyse transport investments, and can also be used for transport operation planning and capacity allocation. If it is to be used for resolving capacity conflicts, however, it is important to know whether transit agencies' timetable requests are consistent with the cost-benefit framework, which is based on passenger preferences. We show how a public transport agency's implicit valuations of waiting time and crowding can be estimated by analysing timetables, apply the method to commuter train timetables in Stockholm, and compare the implicit valuations to the corresponding passenger valuations in the official Swedish cost-benefit analysis guidelines. The results suggest that the agency puts a slightly lower value on waiting time and crowding than the passenger valuations codified in the official guidelines. We discuss possible reasons for this and implications for using cost-benefit analysis for capacity allocation. We also find that optimal frequencies are more sensitive to the waiting time valuation than to that of crowding.

Benefits and Challenges Associated with the Implementation of Lean in Transportation Agencies

The highway construction industry constantly looks to increase productivity while public transportation agencies face challenges of shrinking budgets and staff numbers, even as the work continues to increase in size and demand. As a paradigm to maximize value and minimize waste with the resources available, Lean offers promise to enhance productivity, quality, and service. This study investigates the implementation of Lean by evaluating Lean conditions, practices, benefits, and challenges as expressed by U.S. and UK public transportation highway agencies as well as comparing the implementation of Lean between these agencies. Using a comprehensive literature review and formal interviews with six state departments of transportation along with Highways England from the UK, the findings show that Lean is intended to make process improvements to boost products and services to consumers. By creating a culture of continuous improvement, transportation agencies can improve their processes and increase their efficiencies and performance. Conversely, transportation agencies struggle to educate their employees and stakeholders in their understanding and practices of Lean, which causes confusion and ineffectiveness. Comparing the U.S. and UK transportation agencies, the approach to Lean implementation differs from a top-down approach for U.S. agencies to a bottom-up approach for Highways England. Furthermore, Highways England has an established tool for measuring Lean while development of quantifiable Lean measures is in its infancy in U.S. agencies. Overall, while the sample is limited, this exploratory study provides information for transportation agencies to use in consideration of implementing Lean.

COVID-19 and public transport demand trends in Sicily: analyzing external factors and governmental recommendations

The year 2020 has brought changes in transport services and travel demand globally due to restrictions related to the spread of the COVID-19 pandemic, especially in public transport. The present work focuses on Sicily (Southern Italy). Through the elaboration and administration of an online questionnaire, a sample of 700 regular public transport users was characterized from a socio-demographic point of view, identifying their travel habits, and their opinion on governmental and public transport company initiatives. Eight (8) national government strategies were evaluated on a Likert scale, and ten (10) pandemic mitigation factors were assessed in different phases of the pandemic, highlighting which of these factors were most preferred by the sample on a hierarchical scale. The results lay the groundwork for identifying critical challenges when dealing with such severely restricted mobility conditions and suggesting improvements for public transport agencies and local authorities.

Impacts of long-term service disruptions on passenger travel behaviour: A smart card analysis from the Greater Copenhagen area

Disruptions in public transport are a major source of frustration for passengers and result in lower public transport usage. Previous studies on the effect of disruptions on passenger travel behaviour have mainly focused on shorter disruptions, while the few studies on impacts of long-term disruptions have had limited focus on individual passenger behaviour. This paper fills the gap in research by proposing a novel methodology based on smart card data for analysing the impacts of long-term planned disruptions on passenger travel behaviour. We use k-means clustering to group passengers based on their travel behaviour before and after the closure. We can thus observe how different passenger groups changed travel behaviour after the disruption. We compare these observations to a group of reference lines without disruption to account for general trends. Using hierarchical clustering of daily travel patterns, we are able to in-depth analyse the reactions of certain passenger groups to the disruption. We apply the method on a 3-month closure of a rail line in the Greater Copenhagen area. The results suggest that, in particular, passengers with an everyday commuting behaviour have decreased after the disruption. The proposed methodology enables explicit analysis of the impact of disruptions on diverse passengers segments, while the specific results are useful for public transport agencies when planning long-term maintenance projects.

Vehicle dispatching plan for minimizing passenger waiting time in a corridor with buses of different sizes: Model formulation and solution approaches

Urban public transportation agencies sometimes have to operate mixing vehicles of different sizes on their routes, due to resource limitations or historical reasons. Services with different passenger-carrying capacities are provided to passengers during a mixed-fleet operation. A fundamental question arising here is how to optimally deploy a given fleet of different bus sizes to provide services that minimize passenger waiting time. We formulate a mixed-fleet vehicle dispatching problem as a Mixed-Integer Nonlinear Programming (MINLP) model to optimize dispatching schemes (dispatching orders and times) when a given set of buses of different sizes are available to serve demand along a route. The objective is to minimize the average passenger waiting time under time-dependent demand volumes. Stochastic travel times between stops and vehicle capacity constraints (i.e., introducing extra waiting time due to denied boarding) are explicitly modeled. A Simulated Annealing (SA) algorithm coupled with a Monte Carlo simulation framework is developed to solve large real-world instances in the presence of stochastic travel times. Results show that, in addition to dispatching headway, bus dispatching sequence can strongly affect waiting times under a mixed-fleet operation. Indeed, with an optimal dispatching sequence, a more accurate adjustment of supply to demand is possible in accordance with time-dependent demand conditions, and the total savings in waiting time are mainly driven by a further reduction in the number of passengers left behind. The optimality of uneven dispatching headways stems from two elements: having a mixed fleet and having localized peaks on demand that make buses run full.

Interactive Visual Exploration of Human Mobility Correlation Based on Smart Card Data

Public transportation agencies call for an intuitive, interactive, and reusable visualization tool to detect patterns of crime (i.e. pickpockets and gangs) or missing commuters on public transportation systems. Few existing visualization techniques have visually explored mobility correlations of targets and their companions, who are characterized in diverse mobility types, by using discrete travel hints extracted from a massive amount of data. To fill this gap, a visual analytical system is provided to conduct a group-based and individual-based exploration of mobility correlations of passengers of interest, based on an auto integration of multiple queries. How passengers differ from or correlate with each other are further examined based on their spatiotemporal distributions in trajectories and ODs. Real-world case studies, as well as user feedback made by 30 participants, demonstrate the effectiveness of the system in detecting specific targets and their companions featured in diverse mobility types, or in characterizing their spatiotemporal aggregation patterns for a further tracking on public transportation systems.

Curbspace Management Challenges and Opportunities from Public and Private Sector Perspectives

With rapid growth in on-demand delivery, ridehailing, and shared mobility use, cities are observing dramatic increases in demand for curbspace. In response, cities and private companies have proposed a diverse range of structural, policy and technology solutions to manage the curb lane more efficiently. Through structured interviews with public agency and private company staff and a review of existing pilot project evaluations and curb management guidelines, this study surveys contemporary approaches to curbspace management in 14 U.S. cities and documents the challenges and opportunities associated with them. A total of 17 public agencies (including public works departments, transportation agencies, and metropolitan planning organizations) in every census region of the U.S.A., and 10 technology companies were interviewed. The results show that the top curb management concerns among public officials are enforcement and communication, data collection and management, and interagency coordination. Interviewees reported success with policies such as allocating zones for passenger pick-ups and drop-offs, incentives for off-peak delivery, and requiring data sharing in exchange for reservable or additional curb spaces. Technology company representatives discussed new tools and technologies for curb management, including smart parking reservation systems, occupancy sensors and cameras, and automated enforcement. Both public and private sector staff expressed a desire for citywide policy goals around curb management, more consistent curb regulations across jurisdictions, and a common data standard for encoding curb information.

Equity-Advancing Practices at Public Transit Agencies in the United States

Consistent with federal anti-discrimination and environmental justice law and guidance, public transportation agencies must evaluate the extent to which their decisions benefit and burden different people and groups and they must also conduct public engagement during decision-making. Assessing benefits and burdens and facilitating engagement are critically important for achieving transportation equity. In practice, quantitative analyses of plan impacts can be used to highlight and mitigate disparate benefits and burdens, but analyses are often conducted after major decisions have already been made and agencies have substantial flexibility in data collection and analysis. Public engagement can also affect equity-related outcomes, but agencies still rely heavily on public meetings and one-way information dissemination approaches that alienate potential participants. Considering the shortcomings associated with quantitative analysis and traditional public engagement as well as the open-ended nature of existing regulatory guidance, there is a need to understand the broad range of approaches that public transit agencies use to pursue equity-related goals. In this review of practice, we summarize six measures that agencies are using to advance transportation equity. Each measure is described using information gleaned from semi-structured interviews and primary source materials. We also identify challenges and shortcomings inherent in each approach using perspectives from the academic literature. The results will be useful for practitioners seeking equitable public transit systems and desiring to go beyond the standard approaches suggested by federal law and guidance.

Short-Term Prediction of Demand for Ride-Hailing Services: A Deep Learning Approach

As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve traffic congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning convolutional neural network for short-time prediction of demand for ride-hailing services. Exploiting traditional time series approaches for this problem is challenging due to strong surges and declines in pickups, as well as spatial concentrations of demand. This leads to pickup patterns that are unevenly distributed over time and space. UberNet employs a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. Specifically, the proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. We use a number of features suggested by the transport operations and travel behaviour research areas as being relevant to passenger demand prediction, e.g., weather, temporal factors, socioeconomic and demographics characteristics, as well as travel-to-work, built environment and social factors such as crime level, within a multivariate framework, that leads to operational and policy insights for multiple communities: the ride-hailing operator, passengers, third-part location-based service providers and revenue opportunities to drivers, and transport operators such as road traffic authorities, and public transport agencies. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet’s prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators of travel behavior in making real-time demand predictions for ride-hailing services.

Impact of carsharing on the mobility of lower-income populations in California

Mobility is an important quality of life element, with automobile ownership regularly playing an essential role in the mobility of lower-income populations. While carsharing has gained significant momentum in the past decade as an alternative to private vehicle ownership, less is known about the role of carsharing as a mobility mode for lower-income households. To address this gap, this paper examines the impact of carsharing on the mobility of lower-income populations. This study analyzes data from the 2012 California Household Travel Survey by employing a Structural Equation Modeling (SEM), which controls for residential self-selection, car-ownership and carsharing endogeneity issues, and simultaneity bias. Model results indicate that while carsharing membership enhances mobility for both lower- and higher-income households, it has a more substantial effect on mobility among lower-income families, primarily when it is coupled with public transportation services. The findings of this study underscore the importance of the coordination between carsharing operators and public transportation agencies for enhancing the mobility of lower-income populations.

Factors Affecting Workforce Resilience in Public Transportation Agencies

AbstractEnsuring the resiliency of a nation’s transportation infrastructure network requires a skilled and dependable workforce; this relies heavily on a transportation agency’s ability to recruit ...

Characteristics of Non-Punitive Employee Safety Reporting Systems for Public Transportation as Abridged from TCRP Report 218

The objective of this research is to produce a compilation of best practices used in non-punitive employee safety reporting (ESR) systems at transit agencies, including examples of how ESR systems benefit transit agencies and their employees. This report will support the public transportation industry’s efforts to institute non-punitive ESR as a critical element in safety management systems implementation. The literature review and background research framed the subsequent narrative and findings from interviews with public transportation agencies. For the purposes of this report, the agency that implemented the ESR system determined successful elements. The researchers did not perform a statistical modeling or evaluation method to determine elemental success; rather, success determination stemmed from the implementing agencies. This report also identifies challenges faced through the implementation phases of ESR system deployment, as presented through the literature review and transit agency case studies. Report findings identify benefits associated with wide dissemination of commonly reported hazards and methods to address them, such as the Aviation Safety Reporting System, or the Confidential Close Call Reporting System. There are also recognized benefits in third-party administration and management of ESR systems through reduced likelihoods of associated punitive or retaliatory consequences. Therefore, researchers determined the public transportation industry could benefit from central repository reporting options for hazards and near-miss information aggregation to further support data-driven decision-making. Additionally, industry evidentiary protections would ensure greater reporting. Finally, the public transportation industry would benefit from a non-punitive ESR toolkit or online resource repository that includes samples for agency customization.