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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>
    <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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Demand-dependent Transit Fare Structure to Alleviate Peak-hour Crowding</title>
      <link>https://trid.trb.org/View/2681756</link>
      <description><![CDATA[Crowding in rush hour on urban railways has been a long-standing issue, unaffected even by COVID-19. As a soft measure, discounts for peak spreading have been implemented in a few major cities since the early 2000s, but not all of them have had a significant impact due to the criteria and the level of discount. This paper explores the possibility of implementing a demand-dependent fare system in currently crowded transit networks. From a simple study of one OD pair, we extend the implementation of this fare into the observed demand data of JR railway in Tokyo, Japan. Assuming passengers are currently traveling at an ideal time to their destinations, we found that there is a level of demand-dependent fare that keeps the operator’s revenue the same, and a level of such fare that minimizes the peak-hour demand. This fare might also reduce travel time marginally since the heavy crowding has made the current peak-hour travel time occasionally higher than shortly before or after. Operators can explore the possibility of implementing such fares when they reach a budget limit or headway capacity as a measure to encourage and discourage passengers to travel at dispersed hours.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681756</guid>
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    <item>
      <title>The implications of drivers’ ride acceptance decisions on the operations of ride-sourcing platforms</title>
      <link>https://trid.trb.org/View/2486934</link>
      <description><![CDATA[As a two-sided digital platform, ride-sourcing has disruptively penetrated the mobility market. Ride-sourcing companies provide door-to-door transport services by connecting passengers with independent service suppliers labelled as “driver-partners”. Once a passenger submits a ride request, the platform attempts to match the request with a nearby available driver. Drivers have the freedom to accept or decline ride requests. The consequences of this decision, which is made at the operation level, have remained largely unknown in the literature. Using agent-based simulation modelling on the realistic case study of the city of Amsterdam, the Netherlands, the authors study the impacts of drivers’ ride acceptance behaviour, estimated from unique empirical data, on the ride-sourcing system where the platform applies regular and surge pricing strategies, and riders may revoke their requests and reject the received offers. Furthermore, the authors delve into the implications of various supply–demand intensities, a centralised fleet (i.e., mandatory acceptance on each ride request) versus a decentralised fleet (i.e., ride acceptance decision by each driver), ride acceptance rates, and surge pricing settings. The authors find that the ride acceptance decision of ride-sourcing drivers has far-reaching consequences for system performance in terms of passengers’ waiting time, driver’s revenue, operating costs, and profit, all of which are highly dependent on the ratio between demand and supply. As the system undergoes a transition from undersupplied (i.e., real-time demand locally exceeds available drivers) to balanced and then oversupplied state (i.e., more available drivers than real-time demand), ride acceptance decisions result in higher income inequality. A high acceptance rate among drivers may lead to more rides, but it does not necessarily increase their profit. Surge pricing is found to be asymmetrically in favour of all the parties despite adverse effects on the demand side due to higher trip fare. This study offers insights into both the aggregated and disaggregated levels of ride-sourcing system operations and outlines a series of transport policy and practice implications in cities that offer such ride-sourcing systems.]]></description>
      <pubDate>Wed, 29 Jan 2025 16:55:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2486934</guid>
    </item>
    <item>
      <title>Exploring travelers’ responses to a prepeak discount fare policy and optimizing the pricing strategy to ease peak congestion: The case of Beijing subway</title>
      <link>https://trid.trb.org/View/2463864</link>
      <description><![CDATA[Time-based differential pricing strategy is an effective approach to spread the travel demand during peak hours. To reduce morning peak congestion, a trial prepeak discount fare policy was implemented in Beijing subway in the year of 2016. Regrettably, the intended outcome was not realized. This work aims to explore reasons for restricting the effect of the policy, and an optimization model is established to provide a better differential fare plan. Firstly, smart card records before and after the policy are utilized to track the travelers who shift their departure times from peak to prepeak, and the fare elasticity of different passenger types on departure time shifting can be measured. Secondly, based on the group-specific elasticity to fare changes, an integer nonlinear programming model is proposed to optimize the prepeak discount fare scheme which contains the discount station, the discount ratio and the deadline of the discount. Finally, a practical study of the Beijing Line BT is conducted to verify the effectiveness and accuracy of the method. Results indicate that the deadline of the discount has a significant influence on the effect of the fare policy. The major reason for limiting the effect of the prepeak discount fare policy in Beijing subway is that the discount deadline of 7:00 a.m. is too early to effectively shift the actual peak demand. Transit agencies should delay the deadline of the discount but be cautious for changing differential fare structures or raising discounts.]]></description>
      <pubDate>Mon, 16 Dec 2024 11:59:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2463864</guid>
    </item>
    <item>
      <title>Pre-peak fare discount policy for managing morning peak demand of interregional bus travel: a case study in Seoul metropolitan area</title>
      <link>https://trid.trb.org/View/2446913</link>
      <description><![CDATA[Pre-peak fare discount policies have become increasingly common in urban transit services and show potential to spread morning peak-hour demand. This study analyzes the departure time choice behavior of interregional bus passengers during morning hours. A mixed logit model is applied to estimate the probability of shifting departure time as a function of fare discount rate, in-vehicle congestion, and other influential factors, accommodating heterogeneity of passenger preferences. The analysis data are obtained through a stated preference survey, which is conducted to passengers who regularly use the interregional bus in Seoul metropolitan area. Choice models are segmented by departure time periods (peak, pre-peak, and post-peak) and occupation types (fixed time worker, flexible time worker, and student). Marginal utilities of the unobserved preferences are estimated to suggest policy implications. From the experimental analysis, target passengers for shifting departure time during peak hour are suggested as flexible time workers and students.]]></description>
      <pubDate>Wed, 27 Nov 2024 13:42:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2446913</guid>
    </item>
    <item>
      <title>Panel data analysis of drivers under an evolving cordon tolling system</title>
      <link>https://trid.trb.org/View/2310860</link>
      <description><![CDATA[This paper analyzes a panel dataset of 4011 anonymized car owners in Norway. The authors observe where they live (on neighborhood-level) and how much they drive. The authors combine this with data on tolling costs. Over the sample period 2017–2020 the authors observe four different tolling regimes, with changing tolling costs for peak and off-peak driving in Oslo for different types of cars. The authors employ both fixed-effects regression and production analysis combined with difference-in-difference methods to analyze effects of cordon policies on driving.The empirical results show that peak and off-peak driving are complementary goods, and consequently that increasing peak tolls can decrease off-peak-driving. Responsiveness varies significantly by geographical location and age, with more responsiveness to the tolls closer to the city center and from elderly drivers. Also, the most noticeable response from the drivers under consideration is shifting more of their driving to the hour before the morning peak charge starts.]]></description>
      <pubDate>Fri, 26 Jan 2024 10:02:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2310860</guid>
    </item>
    <item>
      <title>Research on Time-Based Fare Discount Strategy for Urban Rail Transit Peak Congestion</title>
      <link>https://trid.trb.org/View/2306463</link>
      <description><![CDATA[To alleviate peak-hour congestion in urban rail transit, this study proposes a new off-peak fare discount strategy to incentivize passengers to shift their departure time from peak to off-peak hours. Firstly, a questionnaire survey of Shanghai metro passengers is conducted to analyze their willingness to change departure time under different fare strategies. Secondly, based on the survey results, a time-differentiated fare discount model is constructed, considering both the company’s revenue and passengers’ travel benefits, and with the optimization objective of achieving balanced peak-hour and off-peak-hour train loads throughout the day. Subsequently, a genetic algorithm with nested fmincon functions is designed and combined with the actual data of Shanghai rail transit line 9 for arithmetic analysis. Finally, the effectiveness of the model is validated using the survey data. The research results show that the off-peak fare discount strategy can incentivize 6.88% of passengers traveling in the morning peak and 6.66% of passengers traveling in the evening peak to shift to off-peak travel. This research provides theoretical support and decision-making guidance for implementing time-differentiated pricing in urban rail transit systems.]]></description>
      <pubDate>Fri, 22 Dec 2023 08:46:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2306463</guid>
    </item>
    <item>
      <title>Effects of a price incentive policy on urban rail transit passengers: A case study in Nanjing, China</title>
      <link>https://trid.trb.org/View/2289773</link>
      <description><![CDATA[To alleviate travel congestion at peak periods or on congested routes, some measures for urban rail transit (URT) systems including fare incentive schemes and subsidy policies have been widely adopted. Existing measures can alleviate demand during peak periods or on congested routes to a certain extent, but they do not consider the passengers’ satisfaction with their travel experience. Consequently, this study aims to investigate passengers’ acceptance of a fare incentive policy offering a discounted fare during off-peak periods or on uncongested routes before it is implemented in Nanjing, China. To understand passengers’ acceptance of the policy, this study explores the effects of the policy on passengers’ route choices. Furthermore, the differences among passengers with respect to different travel purposes and travel times in route choices have been analyzed. A revealed preference (RP) survey and a stated preference (SP) survey consisting of 463 samples from URT passengers in Nanjing, China, were conducted and analyzed using a random-parameter multinomial logit (RPMNL) model. Results show that socio-economic variables, travel characteristics, travel cost, departure time, and travel distance significantly affect passengers’ route choices. Furthermore, the route choices of passengers with different travelling times and purposes vary. Commuters are most sensitive to travel cost during off-peak periods, followed by non-commuters who travel during peak periods. Departure time and travel distance most significantly affect the route choices of non-commuters during peak periods and off-peak periods, respectively. The findings of this study could assist transit agencies in designing attractive fare incentive policies for public transit passengers and offer valuable insights into effective demand management strategies. Moreover, the lessons derived from this study may guide the implementation of fare incentive policies in Nanjing, China, and elsewhere.]]></description>
      <pubDate>Thu, 30 Nov 2023 16:13:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2289773</guid>
    </item>
    <item>
      <title>Price versus Commitment: Managing the demand for off-peak train tickets in a field experiment</title>
      <link>https://trid.trb.org/View/2205962</link>
      <description><![CDATA[Using data from a field experiment, the authors provide estimates for the own-price elasticity of train travel in Switzerland. The authors' estimates are based on exogenous changes to the level of discounts for long-distance trains and thus avoid the usual endogeneity problem between demand-dependent discounts. Besides the price, the authors also vary the gap between the early booking period and departure during the experiment, which allows us to recover the relative effectiveness of pricing and timing measures. The authors compute own-price elasticities of around −0.7. Ending the early booking period on midnight of the previous day rather than one hour before departure leads to a decrease in the sale of discount tickets by 18–30%, which is equivalent to a price increase by 26–43%. Last, the authors find that increasing the discount causes people to purchase their tickets at an earlier time, which allows us to quantify the value of commitment. The authors results help design measures for peak-shifting in transport at least societal cost.]]></description>
      <pubDate>Mon, 20 Nov 2023 09:12:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2205962</guid>
    </item>
    <item>
      <title>Travel demand management: The solution to public transit congestion? An ex-ante evaluation of staggered work hours schemes for the Paris region</title>
      <link>https://trid.trb.org/View/2152185</link>
      <description><![CDATA[This paper investigates the congestion relief potential of staggered work hours (SWH) schemes for public transit. An ex-ante evaluation framework is developed, which combines a hybrid assignment model with a travel demand management module to simulate the impact of SWH schemes on travel demand and public transit congestion. The key performance indicators capture not only congestion relief benefits, but also rescheduling costs for users. The methodology is applied to the RER A heavy rail line in Paris, the busiest public transit line in Europe. The authors find that SWH schemes generate congestion relief benefits, as intended, even matching up to telework policies. Yet, such benefits remain moderate, adding up to 20% of total crowding costs for the morning peak period at most. Moreover, substantial rescheduling costs are involved: decreasing the total time standing by 1 h implies shifting fifteen trips by the same amount of time. Regarding policy design, theirresults suggest that shifting few users by a large amount of time is usually more efficient than shifting many users by a smaller amount of time; the latter may even prove counterproductive by merely transferring the peak to some other time in the morning. They also find for the RER A that focusing the SWH scheme on a single station - the biggest trip attractor - yields benefits similar to applying the scheme over the whole line (for a similar level of effort in terms of total timeshift). The developed framework is intended to provide guidance as how to improve the design of future SWH schemes, in particular in times of social distancing and need for reduced crowding.]]></description>
      <pubDate>Tue, 25 Apr 2023 09:49:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2152185</guid>
    </item>
    <item>
      <title>The effects of peak hour and congested area taxi surcharges on customers’ travel decisions: Empirical evidence and policy implications</title>
      <link>https://trid.trb.org/View/1942369</link>
      <description><![CDATA[Under a stationary taxi fare structure, the varying passenger demand for taxis over time and space creates a serious shortage of taxi supply during peak hours and outside the city center. Apart from providing additional supply by increasing taxi fleet size and suppressing overall passenger demand by increasing taxi fare, it is more preferable to address the problem by means of surcharge. A surcharge can be imposed on the taxi customers who take taxis during peak hours and/or travel towards congested areas. A portion of the taxi customers may change their mode choices, arrival times, and taxi drop-off locations to avoid the additional cost. By alleviating the shortage of taxi supply, waiting time can be greatly reduced and thus the service quality can also be improved. In this study, 773 taxi customers were interviewed for their travel decisions under different hypothetical scenarios. A total of 4638 observations were used to calibrate multinomial logit and nested logit models for the analysis. The results of the logit models demonstrate that all six concerning attributes, walk and wait time, taxi travel time, public transport travel time, travel fare, early arrival time, and the number of interchanges involved, are significant to affect the taxi customers’ travel decisions. The results of market segmentation analysis further show the variations in the travel decisions of taxi customers in different segments, including gender, occupation, monthly income, and family car ownership. Sensitivity analyses were carried out, and suggestions on the implementation of a taxi surcharge scheme are provided. The calibrated models, findings, and discussions are believed to be useful for establishing a new taxi fare structure to strike a balance of spatio-temporal taxi demand and supply in the market.]]></description>
      <pubDate>Mon, 27 Jun 2022 17:19:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1942369</guid>
    </item>
    <item>
      <title>Morning Peak-Period Pricing Surcharge of Elderly Passengers Taking Express Buses</title>
      <link>https://trid.trb.org/View/1885576</link>
      <description><![CDATA[This study deals with the elderly fare pricing issue for taking express buses in the morning peak period. As many elderly passengers are not commuters, fare discount policy may not be an opportune option when buses get overcrowded. Imposing surcharge on the elderly becomes a potentially beneficial measure that encourages an appropriate number of elderly passengers to circumvent the most crowded buses. The elderly pricing surcharge problem is formulated as a bilevel model, in which the upper-level model is to make the pricing surcharge decision, and the lower-level model is the equilibrium passenger assignment that represents passengers’ bus choice behavior. It is classified into the special case and the generic case depending on the number of buses that impose surcharge. Several useful properties of two cases are analyzed, and a trial-and-error solution method is later developed to solve these two cases. Numerical experiments show that the elderly pricing surcharge scheme is not always applicable to all the demand scenarios, which owns a certain effective interval.]]></description>
      <pubDate>Mon, 25 Oct 2021 09:16:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/1885576</guid>
    </item>
    <item>
      <title>Spatial-Dynamic Matching Equilibrium Models of New York City Taxi and Uber Markets</title>
      <link>https://trid.trb.org/View/1864373</link>
      <description><![CDATA[With the rapidly changing landscape for taxis, ride-hailing, and ride-sourcing services, public agencies have an urgent need to understand how such new services impact social welfare: impacts of technologies on matching customers to service providers, evaluating ride-sourcing operations, and evaluating surge pricing policy, among others. The authors conduct an empirical study to answer this question for Uber using a dynamic spatial equilibrium taxi-matching model. Given a matching function, the spatial distribution of demand activities, and service coverage, the model outputs equilibrium fleet sizes, matches, and social welfare by zone and time of day. Uber provides pickup data for a specific time period in New York City (NYC). Parameters from the model calibrated from medallion cab (Taxi) data are grafted onto the Uber model to supplement the missing information. The Uber model has a root-mean square error of 7.75 matches/zone/interval, which is approximately an 8.52% error. Spatial distribution of responses in demand to fare hikes or vehicle supply to demand surges measurably differ between NYC Taxi and Uber markets. Baseline estimations of welfare indicate that the NYC Taxi industry generates $495,900 in consumer surplus and $1,022,400 in Taxi profits for the 4-h interval, while for the Uber market, the model estimates $73,300 in consumer surplus and $151,300 in Uber profits during the same interval. Spatial-temporal dynamics resulting from fare hike and congestion fee scenarios are analyzed to determine requirements for allocating the congestion charge revenues toward public transit to maintain or improve upon the same consumer surplus.]]></description>
      <pubDate>Mon, 27 Sep 2021 09:45:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/1864373</guid>
    </item>
    <item>
      <title>Developing Policy Framework of Dynamic Toll Pricing in India</title>
      <link>https://trid.trb.org/View/1767829</link>
      <description><![CDATA[There are 543 toll plazas across the National Highways and State Highways in India and most of them are currently operating under Manual Toll Collection (MTC) system. The Government of India has recently adopted Electronic Toll Collection (ETC) system over MTC system, but, due to the technical constraints of toll plazas, they are either operating with both ETC and MTC systems or with MTC system only. The motive of switching from MTC to ETC is to reduce congestion and delay time. However, due to the fix toll charges for a vehicle type and heterogeneity in traffic mix, the peak hour traffic causes congestion that ultimately returns to enormous delay to users. The present study proposes the policy framework of Dynamic Toll Pricing (DTP) that alleviate the congestion at toll plazas by consisting of shifting traffic volume from peak/congested hours to off-peak/non-congested hours. This type of pricing proved to be one of the methods to control the traffic by elevating the toll in peak hours and thus giving discounts in off-peak hours. Such type of study about dynamic toll is not yet carried out for Indian scenario where traffic is highly heterogeneous. Hence, the main goal of the present study is to evaluate the DTP for the Indian condition and thus to examine the effect of discount and time saving on the shift in peak hour traffic demand. The observations revealed that most of the respondents are willing to shift in the morning peak hour than evening peak hour. The maximum shift was observed for small cars and for 25 % discount in toll charges. Further, the price elasticities of demand are calculated and are found to vary between -0.03 and -1.19 i.e. with 1 percent of discount the traffic volume changes between 0.03 % to 1.19 %. The output of the present study may set up a framework for DTP in India and may be used for analysing the travel choice behaviour models in future studies.]]></description>
      <pubDate>Fri, 19 Feb 2021 10:31:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1767829</guid>
    </item>
    <item>
      <title>Managing rail transit peak-hour congestion with step fare schemes</title>
      <link>https://trid.trb.org/View/1755477</link>
      <description><![CDATA[In this paper, the authors propose step fare schemes for a rail transit line connecting a residential district with a central business district (CBD). The proposed fare schemes aim to reduce the total system cost (TSC) while ensuring revenue-neutrality for the rail operator. The user equilibrium (UE) principle in terms of generalized trip cost is adopted to formulate commuters’ departure time choice problems with late arrival under the proposed fare schemes. The equilibrium flow patterns and trip costs are analytically derived. It is found that passengers’ departure times present more even distribution under the schemes with more fare steps, resulting in a lower TSC. Besides, the optimal train schedule and the optimal number and capacity of trains are analyzed. Finally, numerical examples are provided to illustrate the performance of the proposed transit fare schemes.]]></description>
      <pubDate>Mon, 11 Jan 2021 11:09:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1755477</guid>
    </item>
    <item>
      <title>Examining impacts of time-based pricing strategies in public transportation: A study of Singapore</title>
      <link>https://trid.trb.org/View/1736531</link>
      <description><![CDATA[Peak and off-peak pricing strategies are an important policy tool used to spread peak demand in public transportation systems. This study uses an agent-based simulator (SimMobility Mid-term) to examine the impact of pricing (off-peak fare discounts) strategies used in Singapore. The aim of the paper is to demonstrate the capabilities of the simulator, and types of detailed performance indicators it can provide, in order to examine the effects of complex public transport pricing policies. Behavioral models within the simulator are calibrated with relevant datasets such as household travel survey, smart card, GPS probe data from taxis and traffic counts for the Singapore network. Nine (09) time-based pricing strategies are examined that consist of a combination of free pre-peak travel on Mass Rapid Transit (MRT) and an off-peak discount for integrated transit (public buses, MRT and Light Rail Transit (LRT)).Changes in public transport ridership, mode shares, operator's revenue and denied boarding are used as indicators to examine the impacts of pricing strategies. The effects of these policies are also examined on segments of the population in terms of income level, person type and gender. Results indicate that off-peak discounts spread PM peak demand and attract individuals to public transportation. However, the availability of fare discounts in all off-peak periods results in adverse impacts during the AM peak because many commuters shift the return leg rather than the initial leg of their journey. The study concludes with suggestions on how to explore more effective pricing strategies, i.e. providing fare discounts only during off-peak periods that surround AM peak.]]></description>
      <pubDate>Fri, 25 Sep 2020 17:30:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1736531</guid>
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