Passenger Flows in Underground Railway Stations and Platforms, MTI Report 12-43

Urban rail systems are designed to carry large volumes of people into and out of major activity centers. As a result, the stations at these major activity centers are often crowded with boarding and alighting passengers, resulting in passenger inconvenience, delays, and at times danger. This study examines the planning and analysis of station passenger queuing and flows to offer rail transit station designers and transit system operators guidance on how to best accommodate and manage their rail passengers. The objectives of the study are to: 1) Understand the particular infrastructural, operational, behavioral, and spatial factors that affect and may constrain passenger queuing and flows in different types of rail transit stations; 2) Identify, compare, and evaluate practices for efficient, expedient, and safe passenger flows in different types of station environments and during typical (rush hour) and atypical (evacuations, station maintenance/ refurbishment) situations; and 3) Compile short-, medium-, and long-term recommendations for optimizing passenger flows in different station environments

Moving from Walkability? Evaluation Traditional and Merging Data Sources for Evaluating Changes in Campus-Generated Greenhouse Gas Emissions

Universities are increasingly committing to reduce campus-generated greenhouse gas emissions, whether voluntarily or in response to a legal mandate. As an initial step to keeping these commitments, universities need an accounting of baseline greenhouse gas emissions levels and means of monitoring changes in campus-generated greenhouse gas emissions over time. Commute-generated greenhouse gas emissions from travel to and from campus by students and employees are among the most difficult to quantify. This report examines some of the challenges associated with estimating campus-generated greenhouse gas emissions and evaluates ways to address those challenges. The purpose of this study is to identify changes in campus-generated travel behavior at California Polytechnic State University based on the results of three successive campus-wide travel surveys; to evaluate alternative data sources that have the potential to supplement or replace campus travel surveys as a source of data for campus-generated greenhouse gas emissions; and to evaluate alternate methods to estimating greenhouse gas emissions from campus-generated vehicle miles traveled, depending on the presence of campus-specific information about vehicle fleet characteristics. The results of successive travel surveys suggest that the campus population has become more car-dependent over time. Comparison of survey results with data collected from automating traffic counting devices and mobile device data suggest that surveys that are limited to members of the campus community are likely to undercount campus-generated vehicle miles traveled by excluding infrequent, but potentially long, trips by campus visitors. Finally, we find that using campus-specific information on the model years of vehicles used to commute to campus yields higher estimates of campus-generated greenhouse gas emissions, relative to average regional emissions rates

Measuring Success for Safe Routes to School Programs

Safe Routes to School (SRTS) programs aim to increase the share of students who commute to school by active modes (e.g., walking and cycling). The goal of this work was to assess the effectiveness of SRTS programs. Towards that end, we analyzed the California Household Travel Survey (CHTS) data from the four counties in the San Francisco Bay area. We estimated logistic regression model(s) to predict the likelihood that a child commutes to school by active modes based on the presence of an SRTS program and controlling for individual, household, and tract characteristics. Findings indicate that longer trip distance and race (relative to White students) are associated with reduced rates of active travel to school. The presence of SRTS programs mitigates these differences. We conclude that the effect of SRTS programs might best be described as reducing barriers to active school travel, rather than simply increasing the likelihood of using active modes. We also interviewed parents and school administrators about the SRTS programs. The interviewees noted the importance of social connections among students and their families as an advantage of SRTS programs in addition to the health, economic, and environmental benefits. The barriers to more active travel to school cited by the interviewees included the challenge of implementing SRTS programs consistently over a sustained period and the lack of physical infrastructure that feels safe to the students and their parents

Safety Considerations for All Road Users on Edge Lane Roads

ZSB12017-SJAUXEdge lane roads (ELRs), also known as advisory bike lanes or advisory shoulders, are a type of shared street where two-way motor vehicle (MV) traffic shares a single center lane, and edge lanes on either side are preferentially reserved for vulnerable road users (VRUs). This work comprises a literature review, an investigation of ELRs\u2019 operational characteristics and potential road user interactions via simulation, and a study of crash data from existing American and Australian ELRs. The simulation evaluated the impact of various factors (e.g., speed, volume, directional split, etc.) on ELR operation. Results lay the foundation for a siting criterion. Current American siting guidance relies only upon daily traffic volume and speed\u2014an approach that inaccurately models an ELR\u2019s safety. To evaluate the safety of existing ELRs, crash data were collected from ELR installations in the US and Australia. For US installations, Empirical Bayes (EB) analysis resulted in an aggregate CMF of .56 for 11 installations observed over 8 years while serving more than 60 million vehicle trips. The data from the Australian State of Queensland involved rural one-lane, low-volume, higher-speed roads, functionally equivalent to ELRs. As motor vehicle volume grows, these roads are widened to two-lane facilities. While the authors observed low mean crash rates on the one-lane roads, analysis of recently converted (from one-lane to two-lane) facilities showed that several experienced fewer crashes than expected after conversion to two-lane roads

Crystal Balls and Black Boxes: Optimism Bias in Ridership and Cost Forecasts for New Starts Rapid Transit Projects

Several studies have observed an optimistic bias in cost and ridership forecasts for rail transit projects around the globe, which has led to billions of dollars of public investment in projects that have not performed as promised. This bias has been a major cause of concern for project stakeholders, including the Federal Transit Administration (FTA), which has spent an average of over $3 billion each year over the past two decades on new rail transit projects in the United States through its Capital Investment Grants program, commonly known as New Starts. Partly in response to credibility concerns raised by forecast bias, the FTA has made changes to the New Starts program over the years. However, there has been no research to date that has examined how these changes in the New Starts program have influenced forecast accuracy for rail transit projects that receive funding. This study addresses that gap in the literature through a mixed-methods approach involving semi-structured interviews with thirteen transit planning and forecasting professionals and a quantitative analysis of 67 completed transit projects to determine whether and to what extent forecast accuracy has changed over time and what changes in federal policy and transit planning practice might explain these changes. I find that there have been steady improvements over time in the accuracy of ridership forecasts and cost estimates for New Starts projects. The improvement in ridership forecast accuracy can be explained in part by shorter project construction durations and a shift over time in the perceived purpose of forecasting from (1) project promotion to (2) fairness of competition to (3) use in local decision-making. Some of the improvement in cost estimate accuracy can be explained in by changes in project characteristics, particularly a tendency towards more modest projects representing incremental changes to the transit network. This analysis of forecast bias in transit planning gives us reasons for optimism regarding the future of optimism bias in cost and ridership forecast accuracy, since forecasts appear to be on a long-term trajectory toward more accuracy and less bias

Trust in forecasts? Correlates with ridership forecast accuracy for fixed-guideway transit projects

The accuracy of ridership forecasts for fixed-guideway transit projects in the United States has improved in recent decades. A better understanding of the causes for this improvement can help decision makers, project evaluators, and other forecast users identify ridership forecasts that are most likely to be reliable. The analysis in this paper applies a series of linear regression models to evaluate the relationship between ridership forecast accuracy for 67 New Starts projects completed between 1983 and 2012 and four types of project characteristics: time between forecast and observation, local experience with the project mode, physical characteristics, and financial characteristics. The results indicate that local experience and financial characteristics (including the share of a project’s costs funded by federal grants) are not significantly related to forecast accuracy, but there are differences by project mode, where forecasts for commuter rail projects are less accurate than those for other modes. The time until ridership observation does relate to forecast accuracy. However, not all of this elapsed time is important. The length of time required for project planning and development does not have a significant relationship with forecast error, nor does the total time between forecast preparation and ridership observation. However, the length of time required to construct the project is significantly associated with the accuracy of the ridership forecast. These results can help planners, policy makers, and other decision makers make judgments about the degree of trust they should place in transit ridership forecasts

Predictors of Early Adoption of the General Transit Feed Specification

The use of the general transit feed specification (GTFS) data standard has spread rapidly since its introduction in 2007, although it is still not universal in the United States. To explain which transit agencies are likely to have been early adopters of GTFS, we estimate a logistic regression model predicting GTFS adoption based on service area and agency characteristics. We find that agencies with higher ridership and those providing lower shares of a region's total vehicle revenue kilometers have tended to adopt GTFS earlier

Ridership Ramp-Up? Initial Ridership Variation on New Rail Transit Projects

Conventional wisdom within the transit industry suggests that measuring the performance of a transit project immediately after project opening may not capture all the project’s benefits, since it takes time for a project to realize its short-term ridership potential, a process commonly referred to as ridership ramp-up. Though this idea is both intuitive and appealing, especially for projects that seem to be underperforming in their initial years, there is a need for empirical analysis to determine the typical magnitude and extent of ridership ramp-up to better account for ramp-up in ridership forecasting and transit project evaluation. The purpose of this study was to meet this need by evaluating variations in ridership in the initial years after project opening for 55 rail transit projects in the United States. We applied a fixed-effects regression model to predict 1-year increases in ridership in each of the first 5 years after project opening, controlling for variation in gas prices, population, income, and unemployment. We found highly variable and statistically significant increases in ridership in the first 2 years after project opening that may be attributable to ridership ramp-up. These findings could support decisions about how to account for ridership ramp-up in forecasting and performance evaluation for rail transit projects