Development of Guidelines for Collecting Transit Ridership Data

Transit ridership is a critical determinant for many transit applications such as operation optimizations and project prioritization under performance-based funding mechanisms. As a result, the quality of ridership data is of utmost importance to both transit administrative agencies and transit operators. Many transit operators in Virginia report their ridership data to the Department of Rail and Public Transportation (DRPT) and the National Transit Database (NTD). However, with no specific guidelines available to transit agencies in Virginia for collecting ridership data, the heterogeneous mixture of diverse data collection methods and technologies has often raised concerns about the consistency and quality of the reported data. This study investigated the ridership data collection practices adopted by transit agencies in Virginia and developed high-level guidelines to facilitate data collection with improved quality. Specifically, it examined the data collection practices discussed in the literature and those adopted by local transit agencies in Virginia. The research team surveyed 39 transit agencies to obtain a clear understanding of their current practices in data collection scope, technological solutions, sampling and estimation techniques, and data storage and reporting, among others. To evaluate the potential estimation errors based on sampled data, the researchers requested and obtained actual data from five transit agencies of different sizes in Virginia. Comparisons between selected data collection solutions were conducted, and the estimation errors were tested based on different sample data from these agencies. Based on the findings from literature review, surveys, and analysis of actual data, a set of high-level data collection guidelines was proposed. This study recommends that DRPT distribute the developed guidelines among transit agencies in Virginia to help facilitate improved data collection practices across Virginia. It is also recommended that DRPT require the submission of each agency’s ridership data collection methods and correction (adjustment) procedures, in addition to the agency’s reported ridership data

Passengers’ choices in multimodal public transport systems : A study of revealed behaviour and measurement methods

The concept of individual choice is a fundamental aspect when explaining and anticipating behavioural interactions with, and responses to, static and dynamic travel conditions in public transport (PT) systems. However, the empirical rounding of existing models used for forecasting travel demand, which itself is a result of a multitude of individual choices, is often insufficient in terms of detail and accuracy. This thesis explores three aspects, or themes, of PT trips – waiting times, general door-to-door path preferences, with a special emphasis on access and egress trip legs, and service reliability – in order to increase knowledge about how PT passengers interact with PT systems. Using detailed spatiotemporal empirical data from a dedicated survey app and PT fare card transactions, possible cross-sectional relationships between travel conditions and waiting times are analysed, where degrees of mental effort are gauged by an information acquisition proxy. Preferences for complete door-todoorpaths are examined by estimation of full path choice models. Finally, longitudinal effects of changing service reliability are analysed using a biennial panel data approach. The constituent studies conclude that there are otherexplanatory factors than headway that explain waiting times on first boarding stops of PT trips and that possession of knowledge of exact departure times reduces mean waiting times. However, this information factor is not evidentin full path choice, where time and effort-related preferences dominate with a consistent individual preference factor. Finally, a statistically significant on-average adaption to changing service reliability is individual-specific andnon-symmetrical depending on the direction of reliability change, where a relatively large portion of the affected individuals do not appear to respond to small-scale perturbations of reliability while others do, all other thingsbeing equal

Overcoming Barriers for the Wide-scale Adoption of Standardized Real-time Transit Information

In the last few years, a real-time counterpart to GTFS, GTFS-realtime [10], has begun to emerge, with agencies sharing their real-time data in this format. Previously, real-time transit information had only been shared in proprietary formats specific to each vendor or agency. GTFS-realtime offers the opportunity for application developers to create a mobile app that can function across a large number of cities and agencies, and for practitioners and researchers to be able to easily study and compare actual system performance across different transit systems using the same tools, without the overhead of manually transforming data into a consistent format. Having real-time transit data available in a common format is a key pillar for real-time multimodal information systems

Service Reliability Measurement Using Automated Fare Card Data Application to the London Underground

This paper explores the potential of using automated fare card data to quantify the reliability of service as experienced by passengers of rail transit systems. The distribution of individual passenger journey times can be accurately estimated for those systems requiring both entry and exit fare card validation. With the use of this information, a set of service reliability measures is developed that can be used to routinely monitor performance, gain insights into the causes of unreliability, and serve as an input into the evaluation of transit service. An estimation methodology is proposed that classifies performance into typical and nonrecurring conditions, which allows analysts to estimate the level of unreliability attributable to incidents. The proposed measures are used to characterize the reliability of one line in the London Underground under typical and incident-affected conditions with the use of data from the Oyster smartcard system for the morning peak period. A validation of the methodology with the use of incident-log data confirms that a large proportion of the unreliability experienced by passengers can be attributed to incidentrelated disruptions. In addition, the study revealed that the perceived reliability component of the typical Underground trip exceeds its platform wait time component and equals about half of its on-train travel time as well as its station access and egress time components, suggesting that sizable improvements in overall service quality can be attained through reliability improvements

Urban mobility data analysis in Montevideo, Uruguay

Transportation systems play a major role in modern urban contexts, where citizens are expected to travel in order to engage in social and economic activities. Understanding the interaction between citizens and transportation systems is crucial for policy-makers that aim to improve mobility in a city. Within the novel paradigm of smart cities, modern urban transportation systems incorporate technologies that generate huge volumes of data in real time, which can be processed to extract valuable information about the mobility of citizens. This thesis studies the public transportation system of Montevideo, Uruguay, following an urban data analysis approach. A thorough analysis of the transportation system and its usage is outlined, which combines several sources of urban data. The analyzed data includes the location of each bus of the transportation system as well as every ticket sold using smart cards during 2015, accounting for over 150 GB of raw data. Furthermore, origin-destination matrices, which describe mobility patterns in the city, are generated by processing geolocalized bus ticket sales data. For this purpose, a destination estimation algorithm is implemented following methodologies from the related literature. The computed results are compared to the ndings of a recent mobility survey, where the proposed approach arises as a viable alternative to obtain up-to-date mobility information. Finally, a visualization web application is presented, which allows conveying the aggregated information in an intuitive way to stakeholders

Data-Driven Optimization of Public Transit Schedule

Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these,this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.Comment: 20 pages, 6 figures, 2 table