<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Dining or Parking? Managing the Curb During COVID-19 and Beyond: An Analysis of the L.A. Al Fresco Program</title>
      <link>https://trid.trb.org/View/2608492</link>
      <description><![CDATA[The temporary L.A. Al Fresco outdoor dining program provided crucial support to restaurants, bars and cafes during the COVID-19 pandemic. This research performs an economic analysis of the program, comparing parking meter revenue with sales tax revenue and compares treatment corridors with Al Fresco to control corridors without Al Fresco. Results show the program has been successful in keeping more than 80% of businesses open during the pandemic. Treatment corridors with Al Fresco generated an increase of $12 million in gross sales in 2022 compared to 2019. The City of Los Angeles stands to benefit economically and socially by transitioning into a permanent L.A. Al Fresco program.]]></description>
      <pubDate>Wed, 05 Nov 2025 17:17:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608492</guid>
    </item>
    <item>
      <title>Beyond Prediction: On-Street Parking Recommendation Using Heterogeneous Graph-Based List-Wise Ranking</title>
      <link>https://trid.trb.org/View/2389719</link>
      <description><![CDATA[To provide real-time parking information, existing studies focus on predicting parking availability, which seems an indirect approach to saving drivers’ cruising time. In this paper, the authors first time propose an on-street parking recommendation (OPR) task to directly recommend parking spaces for a driver. To this end, a learn-to-rank (LTR) based OPR model called OPR-LTR is built. Specifically, parking recommendation is closely related to the “turnover events” (state switching between occupied and vacant) of each parking space, and hence the authors design a highly efficient heterogeneous graph called ESGraph to represent historical and real-time meters’ turnover events as well as geographical relations; afterward, a convolution-based event-then-graph network is used to aggregate and update representations of the heterogeneous graph. A ranking model is further utilized to learn a score function that helps recommend a list of ranked parking spots for a specific on-street parking query. The method is verified using the on-street parking meter data in Hong Kong and San Francisco. By comparing with the other two types of methods: prediction-only and prediction-then-recommendation, the proposed direct-recommendation method achieves satisfactory performance in different metrics. Extensive experiments also demonstrate that the proposed ESGraph and the recommendation model are more efficient in terms of computational efficiency as well as saving drivers’ on-street parking time.]]></description>
      <pubDate>Wed, 23 Oct 2024 11:49:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2389719</guid>
    </item>
    <item>
      <title>On the management of residential on-street parking: Policies and repercussions</title>
      <link>https://trid.trb.org/View/2170272</link>
      <description><![CDATA[Cities usually handle residential parking through on-street parking management and off-street parking requirements. The two can impact each other but are frequently strategized independently. This paper focuses on residential on-street parking management, which is often dealt with through residential parking permit programs. A survey shows that there are two main types of programs: one that gives precedence to residents and another that restricts residential parking, with mainly the latter being politically explosive. Nevertheless, there is scant literature on the policies implemented in different cities internationally. More research efforts could lead to the formulation of sustainable policies and make the procedures more politically acceptable, in particular given new technologies that help eliminate the high installation and maintenance costs of meters and enforcement. The qualitative and quantitative evidence is analyzed to distill practical real-life solutions.]]></description>
      <pubDate>Thu, 25 May 2023 17:41:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2170272</guid>
    </item>
    <item>
      <title>Irrationality in Metered Parking Payment Compliance</title>
      <link>https://trid.trb.org/View/2059164</link>
      <description><![CDATA[The existing parking system assumes that drivers can pay the right price for parking, but the authors find the opposite in a field study (N=567). Drivers either overpay or underpay for parking at on-street parking meters 98% of the time, for 20–30 minutes on average. Such misalignment between parking payments and presumed price can mask the price signal and reduce its power to influence drivers’ behavior and downstream environmental consequences. These findings provide evidence for widespread parking payment inaccuracy and suggest a way forward for change. This research offers important insights for transportation and planning professionals on the future of parking.]]></description>
      <pubDate>Thu, 17 Nov 2022 09:58:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2059164</guid>
    </item>
    <item>
      <title>MePark: Using Meters as Sensors for Citywide On-Street Parking Availability Prediction</title>
      <link>https://trid.trb.org/View/1993939</link>
      <description><![CDATA[Real-time parking availability prediction is of great value to optimize the on-street parking resource utilization and improve traffic conditions, while the expensive costs of the existing parking availability sensing systems have limited their large-scale applications in more cities and areas. This paper presents the MePark system to predict real-time citywide on-street parking availability at fine-grained temporal level based on the readily accessible parking meter transactions data and other context data, together with the parking events data reported from a limited number of specially deployed sensors. The authors design an iterative mechanism to effectively integrate the aggregated inflow prediction and individual parking duration prediction for adequately exploiting the transactions data. Meanwhile, they extract discriminative features from the multi-source data, and combine the multiple-graph convolutional neural network (MGCN) and the long short-term memory (LSTM) network for capturing complex spatio-temporal correlations. The extensive experimental results based on a four-month real-world on-street parking dataset in Shenzhen, China demonstrate the advantages of their approach over various baselines.]]></description>
      <pubDate>Fri, 30 Sep 2022 14:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1993939</guid>
    </item>
    <item>
      <title>The curb : a city's most valuable real estate : parking's leading guru, Dr. Don Shoup, highlights profess in parking management and predicts the future of parking in his new book, Parking and the City</title>
      <link>https://trid.trb.org/View/1589821</link>
      <description><![CDATA[]]></description>
      <pubDate>Fri, 01 Mar 2019 14:19:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/1589821</guid>
    </item>
    <item>
      <title>Equity in Congestion-priced Parking: A Study of SFpark, 2011 to 2013</title>
      <link>https://trid.trb.org/View/1569907</link>
      <description><![CDATA[]]></description>
      <pubDate>Mon, 26 Nov 2018 16:58:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/1569907</guid>
    </item>
    <item>
      <title>If You Price It, Will They Change? Assessing the Effects of Demand-Based Parking Pricing on Customer Behavior in Washington, D.C.</title>
      <link>https://trid.trb.org/View/1496507</link>
      <description><![CDATA[Jurisdictions across the United States have capitalized on advances in the field of “smart parking” to implement demand-based pricing for curbside parking. The District Department of Transportation (DDOT) has built on the work of other jurisdictions to develop a unique parking solution that seeks to use a “minimum viable product” to better manage parking and improve the customer experience. The combination of the District’s innovative “asset-lite approach” and thoughtful project management and communication practices has enabled DDOT to implement a successful pricing program. With three price adjustments implemented and analyzed, data suggests that demand-based pricing has changed customer behavior in the Penn Quarter and Chinatown neighborhoods. Interim results from the project show that occupancy is trending towards targeted levels, while public feedback indicates that DDOT’s approach to demand-based pricing is palatable to District residents, businesses, and visitors. After successfully implementing and evaluating demand-based pricing, DDOT continues to innovate by investigating opportunities to expand the solution to other District neighborhoods.]]></description>
      <pubDate>Thu, 22 Mar 2018 12:03:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1496507</guid>
    </item>
    <item>
      <title>Sensors and the City: Urban Challenges for Parking Occupancy Detection and Pricing</title>
      <link>https://trid.trb.org/View/1496479</link>
      <description><![CDATA[Implementing performance parking using demand-driven hourly parking meter rates and real-time occupancy information improves the customer experience and provides more available on-street parking in selected cities across the United States. The implementation of performance parking is not necessarily simple, however, and planners must overcome a host of challenges posed by the urban environment. This paper discusses an approach with the potential to become the state-of-the-practice for developing real-time availability for on-street parking in an urban environment, including how the District Department of Transportation in Washington, D.C., dealt with challenges related to: sensor communication, on-street activity, special events, limited space, broader mobility issues, parking users, coordinating installation, and interagency coordination. Using lessons learned from this project, jurisdictions will be better prepared to deal with their own unique urban challenges, positioning themselves to capture real-time availability and implement performance-based pricing.]]></description>
      <pubDate>Thu, 22 Mar 2018 12:03:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1496479</guid>
    </item>
    <item>
      <title>Criteria for Installing Curbside Pay-Parking for Engineers and Planners</title>
      <link>https://trid.trb.org/View/1488341</link>
      <description><![CDATA[The final report of the Edmonton Parking Management Study, which identified a number of block faces where the installation of pay-parking was recommended. The City of Edmonton, Alberta, Canada requested that the consultants prepare a set of criteria that can be used to assess where the installation of curbside pay-parking can be justified. This paper outlines the author’s approach to create criteria to meet the city’s needs.]]></description>
      <pubDate>Tue, 28 Nov 2017 09:19:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1488341</guid>
    </item>
    <item>
      <title>Turning meter transactions data into occupancy and payment behavioral information for on-street parking</title>
      <link>https://trid.trb.org/View/1461108</link>
      <description><![CDATA[Over 95% of on-street paid parking stalls are managed by parking meters or kiosks. By analyzing meter transactions data, this paper provides a methodology to estimate on-street time-varying parking occupancy and understand payment behavior in an effective and inexpensive way. The authors propose a probabilistic payment model to simulate individual payment and parking behavior for each parker. Aggregating the payment/parking of all transactions leads to time-varying occupancy estimation. Two data sets are used to evaluate the methodology, parking spaces near Carnegie Mellon University (CMU) campus, and near the Civic Center in San Francisco. The proposed model generally provides reliable estimations of occupancies at a low error rate and substantially outperforms other naive models in the literature. From the results of the experiments the authors find that people generally tend to slightly underpay in CMU area, whereas for Civic Center area, payment behavior varies by time of day and day of week. For Fridays, people generally tend to overpay and stay longer in the mornings, compared to underpaying and parking for shorter durations in the late afternoons. Parkers’ payment behavior, in general, is more variable and noisier around Civic Center than around CMU. Moreover, the authors explore the effective granularity, defined as the highest spatial resolution for this model to perform reliably. For CMU areas, the effective granularity is around 10–20 spaces for each block of streets, while it is 150–200 spaces for the Civic Center area due to more random parking behavior.]]></description>
      <pubDate>Thu, 25 May 2017 13:56:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1461108</guid>
    </item>
    <item>
      <title>Demand-Responsive Pricing on the Cheap: Estimating Parking Occupancy with Meter Payment Data</title>
      <link>https://trid.trb.org/View/1392592</link>
      <description><![CDATA[The SFpark pilot by the San Francisco Municipal Transportation Agency in California was the first large-scale test of demand-responsive parking pricing in a major city. Several evaluations of the pilot showed that the project yielded substantial benefits. However, measuring parking occupancy is critical to implementing demand-responsive pricing. San Francisco relied on wireless in-ground parking sensors to measure parking occupancy for the SFpark pilot, but those sensors met the end of their useful lives and were deactivated. Parking sensors are still a nascent and costly technology that presents a great deal of risk to cities. Yet many cities, including San Francisco, are adopting new parking meters that make meter payment data widely available. Using sensor and meter data from the SFpark pilot, the agency developed a sensor-independent rate adjustment model that estimated parking occupancy by using meter payment data. Although not everyone pays the meter when they park, the model can reliably estimate occupancy to support demand-responsive pricing. This capability allows San Francisco to continue its SFpark program and lays the foundation for other cities to implement demand-responsive pricing and promote the benefits of better parking policy more widely.]]></description>
      <pubDate>Fri, 04 Mar 2016 17:04:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1392592</guid>
    </item>
    <item>
      <title>Toward a More Meterless Parking System: User Demographic Factors Influencing Adoption and Usage of Pay by Cell (PBC) Services in Washington, DC</title>
      <link>https://trid.trb.org/View/1393401</link>
      <description><![CDATA[The District Department of Transportation’s (DDOT) pay by cell (PBC) program for on-street parking has been very successful. Since its launch in July 2011, the program has attracted one million customers, accounting for approximately 10 million transactions and 55 percent of DC’s parking revenues. The operational and maintenance cost of the program is significantly lower than other means of paying for parking, such as coins and credit cards. The program also enjoys a high level of customer satisfaction. DC’s high adoption rates afford DDOT the opportunity to look at meterless parking. DC will be experimenting with removing meters from one side of the street as part of the parkDC: Chinatown/Penn Quarter project. However, for this concept to gain citywide acceptance, the pay by cell program needs to cater to the needs of all customers that park in the District. This paper analyzes the characteristics of customers that use the current pay by cell program; draws inferences about common traits of PBC users and their usage of the PBC system; and starts framing an understanding on the demographics of PBC non-users. Identifying the general demographics of non-users will enable DDOT to develop outreach strategies that encourage adoption of the PBC program by all parkers.]]></description>
      <pubDate>Tue, 16 Feb 2016 15:31:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1393401</guid>
    </item>
    <item>
      <title>The Access Almanac: Making Parking Meters Popular</title>
      <link>https://trid.trb.org/View/1376812</link>
      <description><![CDATA[Many cities suffer from congested curb parking, polluted air, poor public services, and political opposition to parking meters. To solve these problems cities can charge fair market prices for curb parking, spend the revenue to improve public services on the metered streets, and give price discounts for residents, small cars, and clean cars. By changing the politics of parking, cities can meter more of their valuable curb space, producing more money, less traffic, cleaner air, and a cooler planet.]]></description>
      <pubDate>Tue, 29 Dec 2015 09:54:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1376812</guid>
    </item>
    <item>
      <title>Learning to Flex</title>
      <link>https://trid.trb.org/View/1375092</link>
      <description><![CDATA[This article explores the viability and benefits of demand-based parking and flexible meter pricing. The author argues that creating more availability through demand-based parking can reduce traffic congestion and distractions, lead to fewer pedestrian and bicyclists injuries, and reduce altercations as a result of road rage. He considers a number of issues related to pricing and demand, including segmentation, rate increase or decrease increments and parking meter time limits.]]></description>
      <pubDate>Tue, 24 Nov 2015 09:29:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1375092</guid>
    </item>
  </channel>
</rss>