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    <title>Transport Research International Documentation (TRID)</title>
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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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Congestion Pricing for Indian Cities: Challenges and Prospects Based on International Experiences</title>
      <link>https://trid.trb.org/View/2671597</link>
      <description><![CDATA[Congestion pricing has demonstrated significant effectiveness in managing traffic congestion in cities around the world. While numerous cities globally have implemented or explored various congestion pricing schemes, their potential remains largely untapped in Indian cities. This paper investigates the feasibility of congestion pricing as a tool to address traffic issues in India, focusing specifically on the city of Ahmedabad. By analyzing both successful and unsuccessful international cases, the study extracts critical lessons that can inform potential implementations in the Indian context. Through a theoretical assessment, this study identifies three distinct spatial characteristics in Indian urban environments that could facilitate effective congestion pricing: (1) well-defined charging zones, (2) advantageous geographical features, and (3) compact, densely built central business districts. These features, prevalent in Indian cities, could play a vital role in achieving successful outcomes similar to those observed internationally. In addition to spatial considerations, the paper addresses the unique challenges Indian policymakers may encounter, such as garnering public support, ensuring political feasibility, and developing the necessary technical infrastructure. To assist policymakers in overcoming these challenges, the study provides practical recommendations rooted in international best practices. Overall, the research extends the discussions on congestion pricing in the Indian context, resulting in the identification of valuable insights to guide decision-making for the implementation of congestion pricing schemes, ultimately promoting sustainable urban mobility and addressing traffic congestion issues in Indian cities.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671597</guid>
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    <item>
      <title>Equilibrium analysis of corridor problem with joint charging of departure time and travel distance</title>
      <link>https://trid.trb.org/View/2643269</link>
      <description><![CDATA[This paper investigates the impact of joint time and distance charging on many-to-one origin-destination travel behaviour, as a preliminary exploration of congestion charging in the continuous corridor model. Firstly, the equilibrium trip cost is analyzed to capture the departure time selection characteristics and flow dynamics. Secondly, we perform a mathematical analysis to reveal equilibrium flow patterns and deduce the properties of the departure set (set of departure space-time points). Next, we propose an ingenious mathematical approach to solve the continuous corridor model and conduct a numerical analysis to derive the equilibrium solution for three cases. Compared with the no-toll equilibrium, the departure set under congestion charging is still horn-shaped with its tip located somewhere close to the central business district. As a result, the departure time-based charging encourages commuters to leave earlier to avoid congestion, and the joint charging can ensure effective congestion relief while avoiding unfair charges.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643269</guid>
    </item>
    <item>
      <title>Who benefits from autonomous vehicles? Distributional and general-equilibrium effects in a monocentric city with heterogeneous households and tax interactions</title>
      <link>https://trid.trb.org/View/2647914</link>
      <description><![CDATA[I study the welfare and distributional effects of autonomous vehicles (AVs) using a monocentric city model with heterogeneous households and endogenous labor supply. The formulation captures the fact that AVs allow performing activities while commuting. Numerical results show that AVs increase aggregate welfare, but low-skilled households can experience losses if they are initially located further from the CBD and only high-skilled households can afford an AV. This is due to high-skilled households moving to the periphery, causing an increase in housing prices for the low-skilled, among others general-equilibrium effects. Using the revenues from a distance-based road pricing to finance a labor tax cut for the low-skilled results in gains for both skill types, but reaches only 15% of the welfare increase obtained from using the revenues to finance a general labor tax cut. This latter recycling scheme almost doubles the welfare gains from the introduction of AVs, but amplifies its uneven effects.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647914</guid>
    </item>
    <item>
      <title>Is hybrid measure an effective instrument for behavioural modal shift decisions to mitigate traffic congestion</title>
      <link>https://trid.trb.org/View/2659515</link>
      <description><![CDATA[This paper investigates the influence of a hybrid measure (systemic measure with enhanced transit service) on mode choice decisions for the success of congestion pricing (CP). The term hybrid measure is introduced in this research and the effect of this measure has not been given due attention especially in emerging economies. The hybrid measure consists of 1) push measure, i.e., CP; 2) pull measures for public transit, i.e., lesser number of transfers, lower- access, egress and in-vehicle time, reduced transit travel cost, decreased headway; 3) improvements in transit service, i.e., availability of air-conditioning, increased ease of boarding, lesser onboard crowdedness. We conducted a choice experiment considering several CP scenarios and collected 1028 responses with complete information related to commuters' socio-demographics, travel characteristics, etc. Results from multinomial logit modelling analysis indicated that CP coupled with the availability of air conditioning and ease of transit boarding would attract car commuters towards public buses. Seamless and reduced number of transfers to public buses would also play a major role in improving public bus ridership while implementing CP. We found that commuters' WTP is maximum towards the improvement in ease of boarding and reduction in access time attributes. Also, while estimating the modal trade-off among various generated scenarios, we found that public bus has a maximum probability of being preferred (68.5%) for block C second scenario: ease of boarding, availability of air conditioning are maximum, onboard crowdedness, access and egress time are minimum, and congestion charge for car commuters is highest. Thus, it is believed that a hybrid measure may get larger acceptance than a standalone implementation of push/pull/systemic measures to alleviate congestion and decarbonise urban transport network activities.]]></description>
      <pubDate>Tue, 21 Apr 2026 08:28:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659515</guid>
    </item>
    <item>
      <title>Investigating the effects of changing departure times on controlling secondary traffic peaks during the implementation of a congestion charge zone</title>
      <link>https://trid.trb.org/View/2652403</link>
      <description><![CDATA[Transportation demand management policies have the potential to significantly alter individuals’ routine travel behavior. One of the key responses of private car passengers to the implementation of congestion plans is the adjustment of trip departure times. If the management of changing trip departure time shifts is not effective, the emergence of traffic peak periods before and after the plan may exceed current peak traffic levels. A literature review reveals that the investigation of trip departure time adjustments has received limited attention, and behavior regulation strategies to mitigate peak period formation have not been explored. The primary aim of this paper is to develop scenarios integrating transportation demand management strategies to prevent the occurrence of a tipping point. To achieve this, the effects of social and economic factors, travel characteristics, and citizens’ attitudes toward transportation demand management policies on private car passengers’ departure time shifts in the congestion zone have been examined. To estimate the probability of departure time adjustments, 2,256 individuals were interviewed in Shiraz, yielding 13,536 observations through Stated-Preference (SP) analysis. The calibration of the binary logit model has demonstrated that congestion pricing policies, parking fees, reductions in public transportation travel time, and enhancements in bus service quality exert significant influence on departure time modifications. Based on extensive policy considerations, 27 out of the 36 defined scenarios—those generating a peak period outside the congestion plan’s implementation timeframe—have been deemed unsuitable for execution. This paper introduces a novel probability-thresholding framework that operationalizes behavioral model outputs to proactively screen Transport Demand Management (TDM) scenarios for secondary congestion risks — a methodological advancement not previously applied in developing-city contexts.]]></description>
      <pubDate>Thu, 26 Mar 2026 16:59:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652403</guid>
    </item>
    <item>
      <title>Potential Mode Choice and Traffic Improvement in Jakarta Golden Triangle CBD and Its Road Network Vicinity Due to Area-based Congestion Pricing Implementation</title>
      <link>https://trid.trb.org/View/2669873</link>
      <description><![CDATA[This study analyzes the probability of mode change of existing private vehicle users due to the implementation of area-based congestion pricing, then evaluates the road network performance resulted in the vicinity. A small-scale stated preference survey is conducted to car and motorcycle users who have destination within the hypothetical congestion pricing area to demonstrate mode changing by gathering their preferences on defined six mode changing alternatives. Utility functions for car and motorcycle users are developed with cost and time variables to derive the probability of each mode choice with multinominal logit approach. A proposed traffic assignment analysis by incorporating congestion pricing tariff in the link performance function is used to capture the impact of mode choice and detouring traffic caused by congestion pricing imposed. Traffic network simulation shows that implementation of congestion pricing improves road performance within the area, however, it creates route diversion for through traffic to avoid pricing area and tends to worsen off road performance in the vicinity. Out of five congestion pricing tariff combinations simulated, it is found that tariff combination of 30,000 (IDR) for car and 20,000 (IDR) for motorcycle maximizes the road network performance best.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669873</guid>
    </item>
    <item>
      <title>Road price and capacity policies subject to a fiscal constraint in a city</title>
      <link>https://trid.trb.org/View/2640741</link>
      <description><![CDATA[This paper explores the efficient capacity of the bottleneck and road pricing in a city, subject to the fiscal constraint financing the whole urban road network including the bottleneck. To do this, considering that most cities collect their public fund from property tax, we set three regimes: Regime 1, where congestion pricing is imposed with property tax; Regime 2, where the flat per-kilometer charge is imposed with property tax; Regime 3, where floor area ratio (FAR) regulations and flat per-kilometer charge are imposed with property tax. We derive theoretical properties in each regime. First, in Regime 1, even subject to fiscal constraints, the congestion pricing formula is equal to that of Arnott et al. (1990, 1993), but the optimal capacity should be smaller than that in the presence of a lump-sum tax, reflecting the endogenous marginal cost of public funds. As a result, the congestion pricing revenue exceeds the cost of optimizing the bottleneck capacity. In addition, we show that, only in Regime 3, property tax does not generate deadweight losses owing to the imposition of FAR regulation. Finally, setting the regime of property tax only as the base, our quantitative simulations show that Regime 1 has about 90 % of the welfare increase of the first best, Regime 3 has about 50 % of the increase, and Regime 2 has about 15 % of the increase.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640741</guid>
    </item>
    <item>
      <title>Impact of congestion pricing on taxi travel patterns</title>
      <link>https://trid.trb.org/View/2673278</link>
      <description><![CDATA[Urban congestion continues to challenge large metropolitan areas, often exacerbating travel times, air pollution, and economic inefficiencies. In 2025, in an effort to combat these challenges, a major U.S. metropolitan city introduced a congestion pricing policy that targets congestion reduction, emission control, and transit funding. This study used pre- and post-policy taxi trip data to assesses the effectiveness of that policy on taxi travel within the city’s central business district and used a two-prong approach involving Multiple Linear Regression (MLR) and machine learning-based XGBoost models to quantify its impact. Both models were trained on historical data that was gathered prior to the policy’s implementation and incorporated temporal features and external demand signals to forecast trip counts. A comparison of their forecasts with the actual trip counts observed during the policy period revealed a significant reduction in taxi trips within the charged zone, with the XGBoost model providing forecasting accuracy superior to the MLR model. The study also emphasizes the advantages of using machine learning techniques to improve forecasting and evaluations of urban transportation systems policies, providing key insights for cities considering similar interventions.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673278</guid>
    </item>
    <item>
      <title>Road Users' Acceptability of Congestion Pricing Policy: Case Study Of Hanoi</title>
      <link>https://trid.trb.org/View/2646013</link>
      <description><![CDATA[Traffic congestion pricing is an economic tool widely used in big cities to manage urban traffic and reduce pollution. In Hanoi, rapid urbanization and increasing private vehicle ownership have exacerbated congestion issues. This study investigates road users' willingness-to-pay (WTP) and key factors influencing public acceptability of congestion pricing. Results from a stated preference (SP) survey show that 73.2% support congestion pricing, among which 29.5% will support under specific conditions. Key influencing factors include public transport quality and connectivity, transparent revenues usage, convenient payment systems, and reasonable toll rates. The average WTP for private vehicle users is 30,000 VND per trip, with higher acceptance among those with greater financial flexibility. However, concerns remain regarding a potential shift to motorcycles if congestion pricing is implemented without public transport improvements. These findings could provide useful recommendation to Hanoi's authorities as well as other similar cities in developing an effective congestion pricing program.]]></description>
      <pubDate>Thu, 12 Mar 2026 16:30:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646013</guid>
    </item>
    <item>
      <title>Assessing acceptability of push-pull TDM policy packages: Integrating objective and subjective factors via hybrid choice modeling</title>
      <link>https://trid.trb.org/View/2639396</link>
      <description><![CDATA[The acceptability of Transportation Demand Management (TDM) policies significantly impacts their feasibility of implementation in cities. Even if pricing TDM policies (e.g., cordon and parking pricing) effectively reduces private car usage, they will not be adopted unless deemed sufficiently acceptable by citizens. The acceptability of TDM can be influenced by both objective factors (e.g., socio-economic characteristics, trip attributes, and policy levels) and subjective factors (e.g., perceived fairness, freedom, and effectiveness of a policy). A core aim of this study is to assess these effects on policy acceptability simultaneously. In this study, the acceptability of a package of three policies—cordon pricing, parking pricing, and improved public transit access time—was evaluated using face-to-face interview responses from the Central Business District (CBD) of a megacity. An integrated Hybrid Choice Model (HCM) was employed, incorporating latent variables including perceived fairness, freedom, policy effectiveness, and policy acceptability. Results indicate that cordon pricing is the most influential factor in the acceptability of policy packages, and using the pull policy of transit development can enhance the acceptability of both push policies, including cordon and parking pricing. The results of this study show that having a pro-environmental attitude and concern about transportation comfort significantly has an indirect effect on the acceptability of TDM policy packages. Other variables, such as workplace parking access and household car/house values, significantly increase the acceptability of policy packages, while motorcycle ownership reduces it.]]></description>
      <pubDate>Thu, 12 Mar 2026 14:02:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639396</guid>
    </item>
    <item>
      <title>Optimising toll prices based on a dynamic multi-region MFD SUE traffic model: formulation and a case study of Zealand, Denmark</title>
      <link>https://trid.trb.org/View/2636688</link>
      <description><![CDATA[Road use tolling is an effective way of alleviating congestion. Although many tolling models have been developed, there is gap in the research for a model that: i) is dynamic, ii) accounts for the impacts of tolls on travel demand and departure time choice, iii) accounts for stochasticity in travellers’ route choices, iv) is well-behaved, producing continuous outputs, and v) is computationally feasible to apply to real-life large-scale networks. This paper fills this gap, by developing a tolling model based on the dynamic multi-region Macroscopic Fundamental Diagram (MFD) Stochastic User Equilibrium (SUE) traffic model introduced in Duncan et al. (2025). We begin by extending the model to account for elastic demand and departure time choice. Then, we integrate the model within a toll-price optimisation framework, where the tolling scheme is travel-time-based and the objective function maximises social welfare. We first test the model in a small-scale example multi-region MFD system, and then apply it to estimate an optimal toll-price in a real-life large-scale and detailed case study of Zealand, Denmark. Experiments find that the model is well-behaved and produces smooth objective function surfaces with a unique maximum. Travel behaviour implications of tolling are also realistic, where some travellers opt not to travel by car, some change their departure time, and some change their route. Results suggest that tolling could instigate a positive change in travel behaviour to benefit society.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:49:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636688</guid>
    </item>
    <item>
      <title>Dynamic Toll Prediction Using Historical Data on Toll Roads: Case Study of I-95 Interstate Highway</title>
      <link>https://trid.trb.org/View/2562219</link>
      <description><![CDATA[Dynamic toll pricing has become a very important tool for managing traffic conditions on the road, improving the lane usage by using lanes to the maximum capacity and solving traffic congestion problems. Investigating the effectiveness of machine learning models in forecasting dynamic toll costs on the I-95 Interstate Highway will help to solve the ongoing congestion and travel time uncertainty problems. By using historical traffic data and sophisticated predictive modeling approaches, the project aims to create realistic models to forecast toll prices and travel time variations, so enabling congestion control and toll optimization. By reviewing the advancement of the methodologies used till now based on the recent publications like reinforcement learning hybrid models and optimization frameworks, this research tries to identify the main strengths, limitations, and future opportunities in this research field. The findings shown in this report show the importance of using real-time data on the road and simplifying the computational requirements for the practical deployment of dynamic tolling systems in order to solve the real-life traffic problems.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562219</guid>
    </item>
    <item>
      <title>Optimization of Dynamic Congestion Pricing Strategy—A Case Study of Athens City</title>
      <link>https://trid.trb.org/View/2562135</link>
      <description><![CDATA[A future vision of urban mobility emphasizes better traffic conditions and congestion reduction on the road network in addition to growing urbanization. Traffic congestion and emissions problems, particularly during peak hours, need to be addressed in car-oriented cities. Congestion pricing is an effective demand management tool to reduce congestion and mitigate its impact. The goal of this research is to formulate the optimal congestion pricing strategy by incorporating the system’s dynamic congestion into an optimization model. This study introduces a dynamic congestion pricing strategy to improve system efficiency, which also encourages users to make more effective travel decisions, which helps in traffic management. To formulate the optimization model, this research employs a dynamic traffic assignment model that incorporates the dynamic user equilibrium assignment for the route choice. An objective function is formulated in an optimization model. The objective function considered the dynamic congestion, parameters related to dynamic traffic demand, and traffic emissions. Further, the optimization model is validated using the mesoscopic simulation model of the Athens City networks. We incorporated the results of the simulation in the optimization model in an iterative process. We have utilized a model-based optimization paradigm named Bayesian optimization that tries to model the underlying black-box function based on prior-posterior causality. The output of the simulation and optimization model showed the valuable insights into the traffic parameters, like traffic delay and emissions in the congested areas during the peak hours.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562135</guid>
    </item>
    <item>
      <title>Dynamic Pricing for Shared Autonomous Vehicles in Mobility-as-a-Service: An Integrative Review of Models, Performance, and Policy Trade-offs</title>
      <link>https://trid.trb.org/View/2647772</link>
      <description><![CDATA[Dynamic pricing is the dominant mechanism for managing demand in private ride-hailing platforms, yet its potential application in publicly operated shared autonomous vehicle (SAV) services remains largely theoretical and under-investigated. This paper repositions dynamic pricing within a unified performance framework that links control mechanisms, such as reinforcement learning (RL) and market design, to operational levers for demand shaping and fleet allocation, and to outcomes across economic, operational, accessibility, and environmental dimensions. Using strict inclusion criteria, we systematically review 49 peer-reviewed studies and integrate their findings into a coherent account of how dynamic pricing affects system performance. Analytical, control, RL and market design models report revenue and reliability gains. Separate studies employing these models also document affordability and sustainability trade-offs when fares rise or rebalancing increases vehicle kilometre travel. Overall, the findings suggest that pricing outcomes depend on how fares interact with matching, rebalancing, and charging policies. The review concludes with a structured research agenda centred on three key areas: designing for public objectives, validating real-world impacts, and ensuring transparency in pricing mechanisms, to guide the development of dynamic pricing as a public policy instrument for equitable, profitable and sustainable mobility.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647772</guid>
    </item>
    <item>
      <title>How Much Do ‘U’ Know About UVARs? (Urban Vehicle Access Regulations): Pedestrian Areas, Congestion Charging and Other Strategies That Reduce Traffic, Improve Safety and Make Cities More Livable</title>
      <link>https://trid.trb.org/View/2576302</link>
      <description><![CDATA[You might have heard of terms like "Low Emission Zones" or "Congestion Pricing," but did you know that they are part of a larger toolkit that cities use to manage urban traffic and reduce pollution? Known collectively as Urban Vehicle Access Regulations (UVARs), these measures vary in approach and impact, offering different opportunities and challenges to create healthier, more efficient, and livable urban spaces. This article introduces UVARs and an online course and encourages you to try it.]]></description>
      <pubDate>Wed, 18 Feb 2026 11:59:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2576302</guid>
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