Transit Vehicle Performance Analysis for Service Continuity/Termination: A Data Envelopment Analysis Approach

Public transit agencies aim to improve services while reducing operating costs. Transit performance analysis, as the main approach used to assess operating cost and revenue, has received much attention in recent decades. Most of such studies focus on macro-level performance analysis by comparing across transit agencies or within a transit agency across different parts of its operation. This macro-level analysis assumes that bus drivers and vehicles have identical performance in terms of production and resource consumption, yet they can vary significantly and the variations directly influence service reliability and operational efficiency. As a result, micro-level vehicle performance analysis is needed for operation optimization. This paper introduces an innovative and effective use of the data envelopment analysis (DEA) approach to estimate, project, and compare the operational efficiency of each transit vehicle. Using the paratransit fleet of Utah Transit Authority (UTA) as a case study, the study demonstrates the varying cost structures and operational efficiencies over time associated with different vehicle types. It shows that such variations and time series analysis can be used to guide prioritization of vehicle procurement and service continuity/termination, which further leads to significant cost savings and improvement in reliability of service. The proposed approach is replicable for any transit fleet with available maintenance and operation data. The proposed method provides transit agencies with data-driven analytics to facilitate the decision-making process

Optimizing the spatio-temporal deployment of battery electric bus system

Environmental concerns due to fossil fuel consumption and emissions drive transportation industry to shift towards low-impact and sustainable energy sources. Public transit system, as an integral part of multimodal transportation ecosystem, has been supporting such shift by exploring the adoption of electric vehicles. In recent years, the advancement in Battery Electric Buses (BEBs) and their supporting infrastructure technology made them a viable replacement for diesel and Compressed Natural Gas (CNG) buses. Yet, it remains a challenge on how to optimally deploy the BEB system due to its unique spatio-temporal characteristics. To fill this gap, this research introduces a spatio-temporal optimization model to identify the optimal deployment strategies for BEB system. The identified spatio-temporal deployment of BEB system can minimize the cost associated with vehicle procurement and charging station allocation, while satisfying transit operation constraints such as maintaining existing bus operation routes and schedules. The proposed method is implemented onto the transit network operated by the Utah Transit Authority (UTA) to showcase its effectiveness. As many transit agencies are testing electric buses and considering the integration of electric buses into future fleet, this research will help transit agencies make informed decisions regarding strategic planning and design of BEB systems

Dynamic transit accessibility and transit gap causality analysis

Public Transit Accessibility (PTA) analysis helps transit agencies and planners identify areas in need of transit service improvements and prioritize transit investments. To evaluate the accessibility of existing transit services and identify access gaps, it is critical to accurately estimate travel times between transit stops, which change throughout the day due to transit schedule variations. Commonly used methods in PTA ignore such temporal fluctuation. Moreover, these methods are unable to elucidate the causes of poor PTA. To address these issues, we first implemented an algorithm to effectively compute travel times at multiple departure times throughout the day in order to enable spatiotemporal PTA analysis. A series of indicators that are intuitive to interpret were developed to determine the varying causes of poor PTA and identify areas with immediate needs for improvements. We showcase the analytical framework using a transit network in the State of Utah operated by the Utah Transit Authority. The analysis is based solely on publicly-available open datasets, which makes it generally adaptable to other transit networks. Results can assist transit agencies with identifying areas in need of service improvement and prioritizing future investments

Genetic Algorithm and Regression-Based Model for Analyzing Fare Payment Structure and Transit Dwell Time

The time that buses spend at stops, also called dwell time (DT), has a direct effect on transit service reliability and operational efficiency. A practical, coherent, and quantitative DT modeling approach is needed to identify the factors that contribute most to DT. Commonly used methods for studying DT to date involve manually collected field data or the use of automatic sensors to gather information on factors influencing DT. These approaches have often suffered from limited sample sizes or the inability to provide information on nonelectronic fare payment methods (e.g., cash payment and prepaid passes), which can contribute significantly to DT. To address these gaps, this study developed a genetic algorithm and regression-based modeling approach first to estimate transit fare transactions that do not have electronic records and then to quantify the effect of a number of factors on DT. Integrating information from multiple data sources, the combined approach of optimization and regression analysis offers a data-driven evaluation of existing fare payment structures and their individual effects on DT. With the 35M bus rapid transit line operated by the Utah Transit Authority as a case study, the method demonstrates the robustness and strong prediction power in DT modeling. Results quantify the magnitude of advantages of offboard over onboard fare collections and offer some insights into the operational effects of station placement, design, and the built environment. The modeling approach is transferable to any transit route or system that is equipped with automatic passenger counters. The fare payment analysis can assist transit agencies with service optimization and performance assessments

An efficient General Transit Feed Specification (GTFS) enabled algorithm for dynamic transit accessibility analysis

<div><p>The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George’s transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis.</p></div

Methodological framework.

<p>Methodological framework.</p

WATT plotted regarding station ID.

<p>WATT plotted regarding station ID.</p

Temporal fluctuation in WATT for Sunset Corner station.

<p>Temporal fluctuation in WATT for Sunset Corner station.</p

Stop-Times, stops, and trips files of GTFS for St. George, UT.

<p>Stop-Times, stops, and trips files of GTFS for St. George, UT.</p

WATT variation throughout the day.

<p>(A) Stations no. 1, 2, and 3, (B) different headways, (C) different operating speed.</p