Data Files: Bi-Objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity

This data supports the research project Bi-objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity and a final report published on NITC’s website. Dataset collected through multiple sources and organized into different formats including CSV format, JSON format, shapefile and code repository. Context: The research project develops a bi-objective model that aims to help transit agencies to optimally deploy BEB while considering both capital investment and environmental equity. The unique spatio-temporal characteristic of BEB system, charging limitations (on-route and in-depot charging), and operational constraints are also considered and incorporated into the model

Enabling Decision-Making in Battery Electric Bus Deployment through Interactive Visualization

The transit industry is rapidly transitioning to battery-electric fleets because of the direct environmental and financial benefits they could offer, such as zero emissions, less noise, and lower maintenance costs. Yet the unique spatiotemporal characteristics associated with transit system charging requirements, as well as various objectives when prioritizing the fleet electrification, requires the system operators and/or decision-makers to fully understand the status of the transit system and energy/power system in order to make informed deployment decisions. A recently completed NITC project, No. 1222 titled An Electric Bus Deployment Framework for Improved Air Quality and Transit Operational Efficiency, developed a bi-objective spatiotemporal optimization model for the strategic deployment of the Battery Electric Bus (BEB) to minimize the cost of purchasing BEBs, on-route and in-depot charging stations, and to maximize environmental equity for disadvantaged populations. As agencies such as the Utah Transit Authority (UTA) adopt the model and results, they desire to have a tool that could enable detailed spatiotemporal monitoring of components for the BEB system (e.g., locations of BEBs, the state-of-charge of batteries, charging station energy consumption at each specific timestamp), so that the integration of BEBs into the power/grid system as well as its operating condition could be better understood. To this end, this Translate Research to Practice grant will support the development of a visualization tool that allows transit operators/planners as well as decision-makers to explore the interdependency of the BEB transit system and energy infrastructure in both spatial and temporal dimensions with high resolution. The tool will be built on the scenario-based optimization modeling effort in NITC Project No. 1222, and allow agencies to make phase-wise (short-, mid-, or long-term) decisions based on investment resources and strategic goals. This project will also develop a guidebook to provide step-by-step guidance on data compilation for BEB analysis, model input, model implementation, and results interpretation. It will further detail how the developed visualization tool is structured and designed to ensure results exploration across transit operation and energy consumption. Both the guidebook and the tool will be directly useful to practitioners to easily implement our optimization model for their own transit network, and allow them to build interactive visualizations to assist with decision-making

Dashcam-Enabled Deep Learning Applications for Airport Runway Pavement Distress Detection

23-8193Pavement distress detection plays a vital role in ensuring the safety and longevity of runway infrastructure. This project presents a comprehensive approach to automate distress detection and geolocation on runway pavement using state-of-the-art deep learning techniques. A Faster R-CNN model is trained to accurately identify and classify various distress types, including longitudinal and transverse cracking, weathering, rutting, and depression. The developed model is deployed on a dataset of high-resolution dashcam images captured along the runway, allowing for real-time detection of distresses. Geolocation techniques are employed to accurately map the distresses onto the runway pavement in real-world coordinates. The system implementation and deployment are discussed, emphasizing the importance of a seamless integration into existing infrastructure. The developed distress detection system offers significant benefits to the Utah Department of Transportation (UDOT) by enabling proactive maintenance planning, optimizing resource allocation, and enhancing runway management capabilities. Future potential for advanced distress analysis, integration with other data sources, and continuous model improvement are also explored. The project showcases the potential of low-cost dashcam solutions combined with deep learning for efficient and cost-effective runway distress detection and management

Enabling Decision-Making in Battery Electric Bus Deployment Through Interactive Visualization

69A3551747112The transit industry is rapidly transitioning to battery-electric fleets because of the direct environmental and financial benefits they could offer, such as zero emissions, less noise, and lower maintenance costs. Yet the unique spatiotemporal characteristics associated with transit system charging requirements, as well as various objectives when prioritizing the fleet electrification, requires the system operators and/or decision-makers to fully understand the status of the transit system and energy/power system in order to make informed deployment decisions. A recently completed NITC project, No. 1222 titled An Electric Bus Deployment Framework for Improved Air Quality and Transit Operational Efficiency, developed a bi-objective spatiotemporal optimization model for the strategic deployment of the Battery Electric Bus (BEB) to minimize the cost of purchasing BEBs, on-route and in-depot charging stations, and to maximize environmental equity for disadvantaged populations. As agencies such as the Utah Transit Authority (UTA) adopt the model and results, they desire to have a tool that could enable detailed spatiotemporal monitoring of components for the BEB system (e.g., locations of BEBs, the state-of-charge of batteries, charging station energy consumption at each specific timestamp), so that the integration of BEBs into the power/grid system as well as its operating condition could be better understood. To this end, this Translate Research to Practice grant will support the development of a visualization tool that allows transit operators/planners as well as decision-makers to explore the interdependency of the BEB transit system and energy infrastructure in both spatial and temporal dimensions with high resolution. The tool will be built on the scenario-based optimization modeling effort in NITC Project No. 1222, and allow agencies to make phase-wise (short-, mid-, or long-term) decisions based on investment resources and strategic goals. This project will also develop a guidebook to provide step-by-step guidance on data compilation for BEB analysis, model input, model implementation, and results interpretation. It will further detail how the developed visualization tool is structured and designed to ensure results exploration across transit operation and energy consumption. Both the guidebook and the tool will be directly useful to practitioners to easily implement our optimization model for their own transit network, and allow them to build interactive visualizations to assist with decision-making

Webinar: Social Transportation Analytic Toolbox (STAT) for Transit Networks

This webinar will present an open-source socio-transportation analytic toolbox (STAT) for public transit system planning. This webinar will consist of a demonstration of the STAT toolbox, for the primary purpose of getting feedback from transit agencies on the tool\u27s usefulness. We are especially interested in hearing about any improvements that would aid transit agencies in implementing it. The STAT toolbox was created in an effort to integrate social media and general transit feed specification (GTFS) data for transit agencies, to aid in evaluating and enhancing the performance of public transit systems. The toolbox enables the integration, analysis, and visualization of two major new open transportation data sources—social media and GTFS data—to support transit decision making. In this webinar, we will introduce how we leveraged machine learning and natural language processing techniques to retrieve Twitter data related to public transit systems and to extract sentence structures to geomap those tweets to their corresponding transit lines/stations. Combined with transit accessibility measures computed using GTFS, STAT is able to identify the mismatch between the services that the agency is providing and what the transit users are experiencing. The project uses Salt Lake City and Portland as case studies to demonstrate the usability of the toolbox and how it can support querying, navigating, and exploring the interactions between transit users and services. The Social-Transportation Analytic Toolbox (STAT) for Transit Networks final report is available online: https://doi.org/10.15760/trec.229https://pdxscholar.library.pdx.edu/trec_webinar/1042/thumbnail.jp

Network Effects of Disruptive Traffic Events

Current traffic management strategies are based on expected conditions caused by recurring congestion (e.g., by time of day, day of week), and can be very effective when provisions are also given for reasonable variations from such expectations. However, traffic variations due to non-recurrent events (e.g., crashes) can be much larger and difficult to predict, making also challenging efforts to identify, measure, and forecast their disruptive effects. This project explores a proactive approach to deploy a tool for managing non-recurrent congestion by identifying and quantifying the effects of disruptive traffic events at a microscopic level using a comprehensive set of data sources. A combination of resources including detailed near-time crash records, high-resolution vehicle detection activations and deactivations, as well as traffic signal phasing and timing, are combined together to build an understanding of standard traffic patterns, store this knowledge, and compare it with new incoming data for event identification. The team explored the use of high-resolution data for this purpose at surface street and arterial levels, and the outcomes from model fittings in such scenarios. Upon deployment using virtual servers and interfaces developed by the University of Utah team, ingestion of daily data and event detection will build up of a library of events and their effects, and this process will continue over time to strengthen the knowledge base on the corridors analyzed. Further outcomes from this research could lead to detailed event-based spatio-temporal congestion and safety models, ultimately enabling informed proactive traffic management and safety countermeasures. This project uses the Salt Lake Valley as a testbed and could open new opportunities for research that relies on the integration of large and disaggregated datasets

Bi-objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity

Public transit, compared with passenger cars, can effectively help conserve energy, reduce air pollution, and optimize flow on roadways. In recent years, Battery Electric Bus (BEB) is receiving an increasing amount of attention from the transit vehicle industry and transit agencies due to recent advances in battery technologies and the direct environmental benefits it can offer (e.g., zero emissions, less noise). However, limited efforts have been attempted on the effective deployment planning of the BEB system due to the unique spatiotemporal features associated with the system itself (e.g., driving range, bus scheduling). In this project, we developed an innovative spatiotemporal analytical framework and web-based visualization platform to assist transit agencies in identifying the optimal deployment strategies for the BEB system by using a combination of mathematical programming methods, GIS-based analysis, and multi-objective optimization techniques. The framework allows transit agencies to optimally phase in BEB infrastructure and deploy the BEB system in a way that can minimize the capital and operational cost of the BEB system while maximizing its environmental benefits (i.e., emission reduction). We engaged two transit agencies - the Utah Transit Authority (UTA) and TriMet, both in the planning phase of BEB deployment - to evaluate the usability of the platform. The web-based visualization platform operationalizes the framework and makes it accessible to transit planners, decision makers and the public. This project fits the NITC theme on increasing access to opportunities, improving multimodal planning, and developing data, models, and tools for better decision making. The research could help transit agencies develop optimal deployment strategies for BEB systems, allowing planners and decision makers to create transportation systems that better serve livable and sustainable communities

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

A solar-powered bus charging infrastructure location problem under charging service degradation

Photovoltaic and energy storage system (PESS) offers a compelling pathway towards boosting green transportation due to its low carbon emissions. This study investigates a solar-powered bus charging infrastructure location problem by considering PESS. A two-stage robust optimization model is formulated to handle the uncertainty of charging service degradation. The first-stage decision is to determine which bus depots are to be upgraded with PESS. The second-stage decision is to conduct emergent bus and energy scheduling when the charging service degrades. Two objectives are optimized simultaneously. The first objective is to maximize the net benefits of PESS during day-to-day operations. The second objective is to minimize the unmet passenger demands during the charging service degradation. We implement a case study using a sub-network of Beijing public transport. The results present pieces of evidence that PESS can lower the daily bus charging costs and improve the service capacity of passengers when the charging service degrades

Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: A dynamic programming approach

It is a common practice for transit lines with fluctuating passenger demands to use demand-driven bus scheduling to reduce passenger waiting time and avoid bus overcrowding. However, current literature on the demand-driven bus scheduling generally assumes fixed bus capacity and exclusively optimizes bus dispatch headways. With the advent of connected and autonomous vehicle technology and the introduction of autonomous minibus/shuttle, the joint design of bus capacity and dispatch headway holds promises to further improving the system efficiency while reducing operating and passenger costs. This paper formulates this problem as an integer nonlinear programming model for transit systems operating with mixed human-driven and autonomous buses. In such mixed operating environment, the model simultaneously considers: (1) dynamic capacity design of autonomous bus, i.e., autonomous buses with varying capacity can be obtained by assembling and/or dissembling multiple autonomous minibuses; (2) trajectory control of autonomous bus, i.e., autonomous bus can dynamically adjust its running time as a function of its forward and backward headways; and (3) stop-level passenger boarding and alighting behavior. The objective of the model is designed to balance the trade-off between the operating costs of dispatching different types of bus and the costs of increased passenger waiting time due to inadequate bus dispatching. The model is solved using a dynamic programming approach. We show that the proposed model is effective in reducing passenger waiting time and total operating cost. Sensitivity analysis is further conducted to explore the impact of miscellaneous factors on optimal dispatching decisions, such as penetration rate of autonomous bus, bus running time variation, and passenger demand level