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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Real-time Information Dissemination for Efficiency in a Robo-taxi System (RIDERS)</title>
      <link>https://rip.trb.org/View/2321642</link>
      <description><![CDATA[The Real-time Information Dissemination for Efficiency in a Robo-taxi System (RIDERS)
project addresses the emerging challenges in shared-use mobility, particularly the congestion and efficiency issues posed by ride-hail services like Uber and Lyft, and the rise of autonomous ride-hailing or robo-taxis. It explores how a robo-taxi fleet, equipped with connected and automated vehicles (CAVs), can mitigate traffic congestion and improve safety by collecting and sharing real-time traffic information in urban networks. The project's goal is twofold: first, to enable continuous, widespread traffic data collection and system monitoring without hindering passenger service; and second, to enhance overall transportation system efficiency and safety through informed operational and routing decisions. This interdisciplinary effort involves collaboration between transportation engineering and computer science, with a focus on developing smart infrastructure and connected systems. Using simulation-based experiments and real-world data from New York City, the project aims to devise effective strategies for vehicle repositioning and routing that account for traffic conditions and service quality. The outcomes will contribute to smarter, safer urban transportation, with findings shared through academic publications, presentations, and a dedicated project webpage.]]></description>
      <pubDate>Fri, 12 Jan 2024 10:57:23 GMT</pubDate>
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      <title>Exploring Older Adults’ Mobility Challenges and Preferences Before and After COVID-19 Pandemic</title>
      <link>https://rip.trb.org/View/1948615</link>
      <description><![CDATA[The world-wide pandemic of COVID-19 have influenced older adults (65+ years) more than any other age groups. Older adults are more likely to have health problems and/or weak immune systems. Hence, they have been at higher risk for severe illness. In addition, the statistics show that older adults have a disproportionate number of deaths in the United States and elsewhere due to the coronavirus. Considering the life threats of this infectious disease among older adults, transportation plays a critical role in maintaining older adults’ safety while providing mobility to meet their essential needs such as access to health care.
Beyond driving, older adults have few mobility options (e.g., using taxi, public transit, Uber/Lyft). During pandemic period such as COVID 19, safe mobility options for older adults become even more limited. In 2019, the share of senior Americans (with 65 years old and above) from the total US population was about 16.5% and is predicted to reach 22% by 2050. It should be noted that this is a significant increase in senior Americans’ share of the total US population compared to 1950 in which merely 8% of the whole population was 65 or above (US Census Bureau, 2020). Since the population in USA is aging (like other developed countries), it is very crucial to examine older road users’ opinions, preferences and needs regarding the necessary transportation options that should be provided (during normal times as well as pandemic periods like COVID-19) to maintain their mobility and quality of life.
Fortunately, autonomous vehicles (AV) technology can provide a safe mode of transportation for older adults and persons with disabilities (especially those who no longer can drive safely) during infectious disease epidemics. Although previous research showed a lack of trust and willingness among older population to use AVs, there is no sufficient evidence about the changes in their attitudes, concerns, and willingness to use AVs including shared automated vehicles (e.g., driverless taxi) after experiencing COVID-19 pandemic.
Considering these gaps in the literature, the primary objectives of this study are to:
(1) Examine older Americans’ mobility challenges to use current transportation modes (e.g., driving private automobile, using public transit, taxi, shared rides such as Uber) before and during the COVID-19 pandemic.
(2) Explore the changes in concerns, preferences, and willingness of older Americans to use autonomous vehicles before and after the pandemic.
(3) Identify and quantify the contributing factors affecting older adults’ willingness and concerns to use different levels of autonomous vehicle technology including shared autonomous vehicles (shared taxi).
It is expected that the results of this study will provide actionable countermeasures to mitigate older adults’ mobility challenges especially during epidemic conditions such as COVID 19. The findings of this study are believed to shed light on key mobility challenges and preferences of older adults, which provide transportation authorities and car manufacturers with valuable insight about the future direction of autonomous vehicles in terms of technological needs, policy, design, and planning.]]></description>
      <pubDate>Fri, 06 May 2022 11:27:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/1948615</guid>
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      <title>Ensuring Child Safety in For-Hire Rideshare Vehicles</title>
      <link>https://rip.trb.org/View/1632552</link>
      <description><![CDATA[Rideshare services provided by companies like Uber and Lyft have become much more popular in recent years. In Q4 2021, Uber reported an average of 19.5 million trips a day worldwide, and Lyft reported 18.7 million active riders (defined as riders who take at least one trip during the quarter). As personal vehicles become less desirable and common among urban residents, rideshare and taxi services will likely continue to become more popular. 
Unfortunately, growing evidence suggests that use of child restraint systems (CRS) in rideshare and taxi vehicles is much lower than usage rates in personal vehicles. Since CRS are a crucial tool in decreasing the risk of crash injury for children, this research aimed to ensure that advances in personal mobility in the U.S. are not accompanied by setbacks in child safety.
The objective of this research was to develop guidance to identify and prioritize the types of behavioral interventions needed to improve child passenger (defined as children under 13 years of age) safety in the for-hire ride share environment, including taxis.]]></description>
      <pubDate>Wed, 26 Jun 2019 12:04:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1632552</guid>
    </item>
    <item>
      <title>R2Deep: Recharging Recommendation System for Electric Taxis based on Deep Learning
</title>
      <link>https://rip.trb.org/View/1489957</link>
      <description><![CDATA[Electric taxi (eTaxi) often face an inherent long waiting and recharging time 
(e.g., in hours) at charging stations. Therefore, where to charge and how long to charge an eTaxi has already emerged as an urgent and crucial problem to be solved for wide deployment of eTaxis. In this project, the research team proposes to develop a recharging recommendation system based on deep learning, called R2Deep, for eTaxi drivers to improve their operational efficiency as well as increase the revenue of both eTaxi drivers and companies. The project has three tasks: 1) analyze the existing eTaxi global positioning system (GPS) trajectory data and convert them into information on the grid maps, which will then be directly fed into deep learning models; 2) utilize deep learning techniques including both Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) to learn latent patterns behind eTaxi data sets and provide real-time suggestions on recharging time and charging stations to eTaxis drivers; 3) evaluate the R2Deep model, analyze its performance (e.g., the recommendation accuracy, increase in the eTaxi drivers and companies’ revenue, average reduction of waiting time at charging stations, etc.) with real world data.
]]></description>
      <pubDate>Tue, 28 Nov 2017 19:24:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/1489957</guid>
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      <title>Techniques for Information Extractions from Compressed GPS Traces</title>
      <link>https://rip.trb.org/View/1355611</link>
      <description><![CDATA[Nowadays, Global Positioning System (GPS) devices are routinely installed in motorized vehicles. These devices generate huge volumes of trace (or trajectory) data, with each trace giving the position (latitude and longitude) of a vehicle over time. GPS traces contain information that is valuable to many stakeholders such as transportation planners, policy analysts and business organizations (e.g. trucking industry and taxi companies). Such traces are often compressed to eliminate redundancy and reduce the amount of storage space. When additional data about vehicles (e.g. freight information and readings from on-board sensors for trucks, fare and occupancy information for taxicabs) is available, the combination of compressed trace data and vehicular data serves as a richer source of information that is useful in multiple application scenarios. The proposed research investigates methods to effectively extract information from large volumes of compressed GPS traces and other vehicular data. The specific tasks which will be carried out during this work are as follows: Task 1: Develop efficient techniques for retrieving from a database of compressed traces, a collection of traces that are similar to a given query trace. Such techniques should also have the capability to classify the retrieved collection of traces according to specified criteria. Task 2: Extend compression techniques for GPS traces so that traces that include other vehicular or sensor data (along with latitude, longitude and time) can also be effectively compressed. As in Task 1, such extensions should allow efficient retrievals of collections of traces that are similar to a given query trace. The proposed work falls under Focus Area 4 ("System modernization through implementation of advanced information technologies") of University Transportation Research Center (UTRC) Region 2. Potential long term benefits of the proposed research include the development of effective methods for extracting useful information from compressed representations of trajectories and other vehicular data. Such methods will be highly beneficial in processing complex queries that are of interest to transportation planners and other stakeholders. The deliverables of this project include software tools, research reports, papers in conferences/journals, a research brief suitable for distribution to policy makers and data sets generated as part of the work. These deliverables will be made available to the research community through an appropriate website.]]></description>
      <pubDate>Wed, 27 May 2015 01:01:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/1355611</guid>
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    <item>
      <title>Taxis in Paratransit: Considering the Ecological Footprint</title>
      <link>https://rip.trb.org/View/1236979</link>
      <description><![CDATA[Many communities in the US and around the world--whether urban, rural, or suburban--are interested in providing effective paratransit services that are also reasonably good performers from the viewpoint of environmental sustainability. Herein, paratransit will be understood in the general sense of flexible passenger transportation that does not follow fixed routes or schedules, and often serves the transportation needs of the handicapped or elderly. Typically, paratransit systems exist because laws and regulations require the services to be provided, and because governments are able to provide the needed subsidies. Public or private operators who deliver paratransit services often do so via fleets of vans or mini-buses. Paratransit ranges widely in terms of the flexibility of the provided services, which can range from relatively inflexible transportation along a more or less defined route, to fully "demand responsive" service that offers door-to-door transportation, on-demand. Given the governmental subsidies needed to support paratransit, policymakers are increasingly considering ways to operate paratransit systems more effectively. Likewise, they are increasingly interested in the environmental sustainability of paratransit. In New York City (NYC), the Metropolitan Transportation Authority - New York City Transit ("NYC Transit") provides paratransit services by administering Access-A-Ride (AAR), which offers three types of services: shared-ride, door-to-door, or feeder service. Feeder service refers to a trip in which AAR transports the customer for the first leg of the trip, from the starting point to a fixed-route bus/subway stop. The AAR service area encompasses the five boroughs as well as a ¾ mile corridor beyond fixed-route service across the NYC borderline into nearby areas of Nassau and Westchester counties. Services are provided by private carriers who contract with NYC Transit and use lift-equipped vans, or sedans. In addition, service is also provided by private taxis, livery and black car services. Attention has been given to the potential financial benefits of such integration, and some have estimated that cost-per-trip can be reduced more than 50% by using yellow cabs instead of the more costly AAR vans. This study will focus on paratransit services provided by NYC Transit, and in particular on the integration of taxis (and livery and black car services) into the mix of service-providing vehicles. The overall focus will be on how such taxi integration affects the environmental sustainability of paratransit systems. A working hypothesis is that there are types and degrees of taxi integration that will provide the same levels of paratransit service, for approximately the same cost, but with a measurably smaller ecological footprint. The project aims to provide a foundation for a follow-up externally-funded study (or studies) that could expand the scope of the analysis in various directions.]]></description>
      <pubDate>Tue, 08 Jan 2013 01:00:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1236979</guid>
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