Results for “RiDER” — Page 5

DOE OSTI2020

Investigating the influence of latent lifestyles on productive travels: Insights into designing autonomous transit system

As a special case of multitasking, travel-based multitasking typically refers to conducting a set of in-vehicle activities while traveling. Travel-based multitasking has an indisputable influence on offering a pleasant travel experience to transit users during their rides, given that they can use their travel time to perform desirable activities and gain benefits in various form. For instance, the in-activities could help the rider free up time from his/her schedule for the day (i.e., a worthwhile use of travel time). In this study, we investigate how the worthwhileness of a travel-based multitasking could be under the influence of: (1) the transit user’s lifestyle, and (2) socio-demographics, and (3) the characteristics of the transit trip. Towards this, we conducted an intercept survey focusing on the transit trips in the Chicago metropolitan area and analyzed it using latent class modeling approach. Per the results, two classes of transit users could be identified: (1) worthwhileness seekers, productively travelers and (2) leisure seekers, occasional worthwhile travelers. The results also suggest travel time, waiting time and walking distance to the transit station, and the set of in-vehicle activities as significant predictors of worthwhile use of travel time. Finally, the findings provide insights to policymakers for improving public transit systems in the current form, as well as designing an autonomous mobility system as the future form of public transit.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Improving the performance of first- and last-mile mobility services through transit coordination, real-time demand prediction, advanced reservations, and trip prioritization

Socio-demographic trends and recent economic development patterns have resulted in travel behavior changes that call for more flexible and accessible public transit options. Because flexible transit services vary in scope, size, and service type, new data-informed methods are useful to optimize services based on the specific needs of local communities and riders. In this study, real-world demand and vehicle trajectory data were used to evaluate and optimize system performance for an existing first-mile–last-mile (FMLM) service in Robinson Township, PA. A general FMLM model for arbitrary demand and service supply was then developed to quantify system performance—both travel time costs and day-to-day reliability—for various operational polices considering spatio-temporal demand variation and transportation network dynamics. Heuristics were used for optimal real-time vehicle routing in sizable real-world networks accommodating various service types and scopes. In this case study, total user costs were reduced by 18.6% when rides were coordinated with mainline fixed-route transit. Predictive routing strategies were shown to marginally improve system performance under sparse and variable spatio-temporal demand. The case study also highlights potentially large travel time and user reliability improvements—reductions of 51% and 53.8%, respectively—when trip requests were made in advance of their desired pickup time. Finally, we show that travel time reliability can be improved for time-inflexible trips with trip prioritization without increasing total user costs. These results were stable to changes in demand density.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Synergies between repositioning and charging strategies for shared autonomous electric vehicle fleets

The emergence of on-demand shared autonomous electric vehicle (SAEV) service requires careful charging station planning and a joint charging and repositioning strategy to mitigate empty travel. This study couples charging and repositioning events as a means of improving service quality (rider wait times), reducing empty travel due to repositioning or charging, and improving fleet utilization (average daily trips per vehicle and charging queues). This synergy is explored for the Austin, Texas region using POLARIS, an agent-based model. On average, wait times were 39% lower, and average daily trips served per SAEV increased up to 6.4 (or 28%) compared to SAEV repositioning with heuristic charging. Coupling repositioning with charging decreased the fleet's percent empty travel on average by 1.6%, relative to the scenario treating them as independent events (which varies by charging station design). Sparser charging stations reduce investment costs, and operators can leverage this framework to keep traveler wait times low.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Calculated covariance matrices for fission product yields using BeoH

Fission product yields (FPY) are important for a variety of applications (reactor neutronics, spent fuel, dosimetry, radiochemistry, etc.) and are currently included in many of the evaluated libraries around the world. The FPYs in the current US evaluation, ENDF/B-VIII.0, are mainly based on the 1994 evaluation of England and Rider and have only had slight updates—such as the inclusion of a 2 MeV point for 239 Pu—since their development. Additionally, only mean values and uncertainties are included in the evaluation, not full correlations. Los Alamos National Laboratory, in collaboration with several other institutes, has been working on an updated evaluation for the FPYs of 239 Pu(n,f), 235 U(n,f), 238 U(n,f), and 252 Cf(sf) using the deterministic, Hauser-Feshbach, fission fragment decay code, BeoH. BeoH calculates the FPYs consistently with many other prompt and delayed fission observables, explicitly taking into account multi-chance fission and ensuring consistency between observables. In addition to providing updated means and uncertainties for the FPYs on a pointwise energy grid from thermal to 20 MeV, we calculate correlations between all FPYs at each incident energy and across incident energies. Here, we discuss the development of these covariance matrices, differences in the correlations between FPYs based on the parameters that are included in the model optimization, and correlations across incident energies for neutron-induced fission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Fission Product Yield Modeling and Evaluation

Although independent and cumulative fission product yields have been a part of evaluated libraries for decades, there have been few updates over the years. The fission product yield sub-library in the ENDF/B-VIII.0 library is still largely based on the evaluation of England and Rider from the mid-90’s, with only more recent updates to the energy dependence of 239 Pu below 2 MeV and fixes to isomeric states and missing fission products. Over the past several years, there have been a wealth of new measurements of independent and cumulative fission product yields, particularly those with short half-lives, and there have been significant improvements in the modeling of prompt and delayed fission observables. Here, we describe recent progress in the improvement of fission product yield calculations, using the BeoH code and the underlying Hauser Fesh-bach Fission Fragment Decay (HF 3 D) model, developed at Los Alamos National Laboratory. We will describe our recent calculations for consistent prompt and delayed fission observables for major and minor actinides, including new work investigating isomeric ratios. We will detail the ongoing evaluation process for energy-dependent fission product yields from thermal up to 20 MeV incident neutron energy and some validation work that has been performed for these new fission product yield calculations. Additionally, we will discuss future perspectives of this work, highlighting the need for additional data.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2024

Optimization of Dynamic Ride-Sharing by Considering User Preference Through Discount and Delay Tolerance

Dynamic ride-sharing (DRS) has been projected to be a key solution to lowering system-wide congestion. Despite recent development progress, demand studies for DRS suggest low levels of willingness for travelers to use such services. The disconnect between DRS system designs and user preferences limits the application impacts of DRS in the real world. Therefore, this paper aims to design a new DRS system by considering the user preferences of choices under different levels of services. In this study, an agent-based approach is used to model a fleet of shared vehicles that allows DRS. An optimization model is developed to match riders to vehicles while accounting for traveler delay and delay acceptance. Travelers are also dynamically issued predictive discounts to incentivize them to accept longer trip delays. Results show that the proposed approach can improve system efficiency by increasing average vehicle occupancy by up to 1.0 persons/trip and DRS acceptance up to 38.9% depending on fleet size. Additionally, congestion is eased through the decrease of empty vehicle miles traveled by up to 7.1%.

Paul, Joseph↗

DOE OSTI2020

Use of Shared Automated Vehicles for First-Mile Last-Mile Service: Micro-Simulation of Rail-Transit Connections in Austin, Texas

Shared fleets of fully automated vehicles (SAVs) coupled with real-time ride-sharing to and from transit stations are of interest to cities and nations in delivering more sustainable transportation systems. By providing first-mile last-mile (FMLM) connections to key transit stations, SAVs can replace walk-to-transit, drive-to-transit, and drive-only trips. Using the SUMO (Simulation of Urban MObility) toolkit, this paper examines mode splits, wait times, and other system features by micro-simulating two fleets of SAVs providing an FMLM ride-sharing service to 10% of central Austin’s trip-makers near five light-rail transit stations. These trips either start or end within two geofenced areas (called automated mobility districts [AMDs]), and travel time and wait time feedbacks affect mode choices. With rail service headways of 15?min, and 15 SAVs serving FMLM connections to and from each AMD, simulations predict that 3.7% of the person-trip-making will shift from driving alone to transit use in a 3?mi?×?6?mi central Austin area. During a 3-h morning peak, 30 SAVs serve about 10 person-trips each (to or from the stations), with 3.4?min average wait time for SAVs, and an average vehicle occupancy of 0.74 persons (per SAV mile-traveled), as a result of empty SAV driving between riders. Sensitivity analysis of transit headways (from 5 to 20?min) and fleet sizes (from 5 to 20 vehicles in each AMD) shows an increase in FMLM mode share with more frequent transit service and larger fleet size, but total travel time served as the biggest determinant in trip-makers’ mode share.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

An On-Demand Electric Transit Case Study of New Rochelle, New York

Here, this article characterizes the performance and ridership patterns of an on-demand transit (ODT) service utilizing lightweight electric vehicles (EVs) in New Rochelle, New York. Ridership sociodemographics, travel patterns (both temporal and spatiotemporal), and energy use from the service were explored using travel and survey data from September 2019 through December 2023. The ODT service was found to be used more by women (nearly 60%) and younger demographics (>65% under the age of 42), with peak use in the middle of the day and a grocery store as a top origin and destination. The service was utilized primarily for short trips (86% under 2 mi), with approximately one-third of riders using the ODT service to connect to a train or bus. The costs associated with fueling/charging were compared for different types of fleet vehicles, and the small, right-sized EVs were found to have annual charging costs that were roughly half of the refueling costs for conventional hybrid vans, and 24 times lower than a fleet of diesel buses. Evaluating the vehicle fleet and mapping current socio-spatial travel demand can inform system performance, guide service area development, and support future planning such as expansion to nearby communities and transit hubs. The findings in this case study suggest that on-demand electric transit may be a significant and growing space for advancing highly valued public mobility services. Public transport interventions that consider right-sized, electric, on-demand vehicles can help improve mobility access and reduce energy use and refueling costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

2000 Southeast Florida Regional Travel Characteristics

The Southeast Florida Regional Travel Characteristics Study (Travel 2000) provided the needed observed travel information for developing planning tools for a three-county region of Southeast Florida (Miami-Dade, Broward, and Palm Beach). Data collection efforts began in December 1998 and were completed in September 1999. Travel logs were collected for 5,168 households, which included all travel by all residents for one day. In addition, approximately 10,000 bus riders were surveyed, and visitors were surveyed at 79 hotels. Trucking information was gathered from 848 commercial establishments using trucks. Workers were surveyed at seven major employment areas in the three counties.

1Hz data↗

DOE OSTI2026

Onboard/Origin-Destination Survey - Phoenix - 2015

Valley Metro conducted an on-board transit survey during spring 2015. This project gathered updated travel behavior data from transit users that encompassed all rail and bus fixed-route services in the Phoenix metropolitan planning area. The study’s goal was to compile statistically accurate information about transit riders and how they use the transit system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Transit Rider/Travel Behavior Inventory Survey - Minneapolis-St. Paul Metro - 2010

The Metropolitan Council administered a system-wide transit on-board survey between September and November 2010 as part of the 2010 Travel Behavior Inventory to provide detailed transit usage patterns and rider information to support modeling and planning efforts. The study’s focus was to capture only the most current and reliable data necessary to determine future public transportation needs in the Minneapolis region. The study was structured to collect detailed transit ridership data for different routes during different times of day to develop a disaggregate transit trip table that will support advanced travel demand modeling. The study was designed to leverage existing data sources, such as the 2005 on-board survey, to provide the best quality data to update the Travel Behavior Inventory in the Minneapolis-Saint Paul metropolitan area.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Ride Pingo to Transit

In this project, we developed an on-demand microtransit first- and last-mile service. To integrate the service with fixed-route transit, we developed a feature called Transit Connect that prioritized riders’ on-time arrival at the transit station over other service requirements. We first prototyped service and related algorithms in a simulated environment, and then piloted the service in the city of Kent, Washington. Our algorithm incorporates request-specific hard drop-off deadlines to ensure timely arrivals for transit transfers. In the pilot, these constraints were obtained from GTFS Realtime data to accurately determine the schedule of the transit and the location of the stations. This approach introduced the ability to accept or decline new requests based on the timing of transit connections for these new requests and connection status of onboarding customers. The pilot (called “Ride Pingo to Transit”) deployed a fleet of three 14-person vans, ran from September 2021 to March 2023, and served a total of 21,329 trips. Transit Connect was offered for drop-offs at both Kent Station and the Kent Valley hub. In total, 2,844 such trips were completed. This dataset was collected from our pilot, which includes the following: - Requests: List of all trip requests, including those that were actually served and those not materialized. - Fleet: Daily vehicle service logs. - Service details: Daily vehicle stop logs (boarding and alighting). - Trip types: First mile, last mile, or point-to-point. ![pingo to transit](pingo-to-transit.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗