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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>Drivers and barriers towards Mobility-as-a-Service (MaaS) in regional and rural areas: insights from service providers and end users</title>
      <link>https://trid.trb.org/View/2367003</link>
      <description><![CDATA[Several commentators have proposed definitions of “Rural MaaS”. For example, Eckhardt et al. describe urban MaaS as multimodal and built around public transport. Rural MaaS, by contrast relies on integrating a more diverse range of services and user groups with an emphasis on car-based modes. In a rural context reducing transport disadvantage will come to the forefront as an important objective for MaaS. While local public transport is unlikely to be the backbone of regional and rural MaaS, long-distance public transport, accessed by shared modes, is likely to be a vital component of the MaaS offer. Drawing on lessons learned from these prior studies this research investigates how MaaS might be delivered in a rural and regional context in Australia with a focus on reducing transport disadvantage. Data collection has involved in-depth interviews with key service provider stakeholders and end-user group discussions with end users. Three regional towns in New South Wales (NSW) were selected for detailed study (Dubbo, Nowra, Coffs Harbour). Findings from the data collection have been used in the development of a “blueprint” for Rural and Regional MaaS focussing on regional towns and their hinterlands.]]></description>
      <pubDate>Mon, 15 Apr 2024 14:20:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2367003</guid>
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    <item>
      <title>“Messaging” and public transport in the COVID-19 environment</title>
      <link>https://trid.trb.org/View/1857849</link>
      <description><![CDATA[Getting the message right has been an important task for public transport operators as the sector seeks to recover from the drastic fall in patronage occasioned by the COVID-19 pandemic. Coming out of any lockdown or periods of restriction must be associated with public transport being still the mode that provides the greatest chance of a sustainable urban future. This paper looks at public transport trends in New South Wales during the first six months of 2020 (i.e. before and after the first wave of COVID-19), considers the “messaging” put in place and asks why On Demand Transport (ODT) seems to be bucking the trend when it comes to reversing the dramatic declines in public transport patronage that have been associated with COVID-19.]]></description>
      <pubDate>Tue, 08 Jun 2021 12:31:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1857849</guid>
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    <item>
      <title>An international review of experiences from on-demand public transport services</title>
      <link>https://trid.trb.org/View/1696271</link>
      <description><![CDATA[The aim of this report is to contribute to develop knowledge about what the developments in positioning and smartphone technology bring to the table for the public transport sector. The overarching question in the report is: can new technology improve demand-responsive transport (DRT)? The cases analysed in this report were selected using a number of criteria to delimit the sample and distinguish the cases from “traditional” DRT and from ride-hailing services. A total of 35 different services were identified that met the criteria for what this report refers to as on-demand public transport. The identified cases are located in nine different countries and 23 different cities or regions, and includes services that have been or are operating in major urban areas, smaller towns, suburbs, semi-rural and rural areas. Nine services, most of which are subsidised by the public sector, have been analysed in more detail. The comparison of the cases reveal differences and similarities concerning aspects such as vehicles and fleet sizes, and service partnerships. Different variants are also described regarding operational policies of the services. This includes origin-destination policies, areas covered by the services, where to pick up and drop off passengers, operating hours, booking method, time of booking, payment and pricing. For the nine cases that are the focus of the report a comparison of patronage, productivity and production costs are also made. A main conclusion from this part of the study is that so far there is scant evidence that new technology improves the productivity of DRT. This suggests that new technology is no panacea for fixing the problems of DRT and the study shows that thus far, at least, on-demand public transport hardly represents a transport revolution.]]></description>
      <pubDate>Fri, 03 Apr 2020 15:48:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1696271</guid>
    </item>
    <item>
      <title>Comparing Parking Strategies of Autonomous Transit On Demand with Varying Transport Demand</title>
      <link>https://trid.trb.org/View/1626476</link>
      <description><![CDATA[Autonomous transit on demand is increasingly considered to become a viable substitute for taxi services. Autonomous vehicles (AVs) can be managed through a centralized controlling system, targeting system optimization rather than user optimality. This centralized control can enable a more efficient, strictly-adhered-to parking strategy to reduce inefficient empty traveling. In this project, four different parking strategies are implemented in the AV extension of MATSim (Multi-agent transport simulation), namely demand-based roaming, parking on the street, parking in depots and a mixed strategy of parking on the street and in depots. The influence of different public transit (PT) demand levels on the different parking strategies was explored, showing that the shared system is robust to varying levels of demand, and that the different parking strategies trade off user convenience for operational cost. The road parking strategy appears to be the best for consolidating rides into larger vehicles, especially for the increased demand scenario.]]></description>
      <pubDate>Tue, 16 Jul 2019 18:04:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/1626476</guid>
    </item>
    <item>
      <title>Behavioral Response to Transit Demand Management Promotions: Sustainability and Implications for Optimal Promotion Design</title>
      <link>https://trid.trb.org/View/1573041</link>
      <description><![CDATA[Increasing ridership in metro systems is outpacing its capacity. Promotion based transit demand management can help agencies better manage the available system capacity when the opportunity and investment to expand are limited. While several studies address short-term behavioral response to such promotions using before and after analysis, how behavioral changes are maintained in the long run is also very important. Using an extensive automated dataset over two years from the Hong Kong Metro system, this paper explores the longitudinal behavior of passengers in response to a promotion to shift their travel time to the pre-peak period. The approach uses customer segmentation to evaluate the response of different groups. The results highlight the heterogenous response of different groups. Users with high schedule flexibility, less variable itineraries of a trip and relatively long distances are more likely to shift their travel times. The longitudinal promotion analysis reveals that 35-40% of passengers who initially shifted will eventually revert to their previous travel time periods. Based on the results of the analysis, an ‘optimal’ promotion design approach is applied to examine the effectiveness of promotion strategies given different response assumptions, and constraints on budget and performance requirements. The promotion design using group-specific response can better target price-sensitive users, hence improves its effectiveness, while the design based on the long-term response shows a significant performance decrease.]]></description>
      <pubDate>Fri, 01 Mar 2019 15:51:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1573041</guid>
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    <item>
      <title>Forecasting Perth’s public transport patronage: econometric analysis and model development</title>
      <link>https://trid.trb.org/View/1457959</link>
      <description><![CDATA[The paper outlines the development and application of a model for forecasting patronage and fare revenues for Perth's public transport services over the medium term. The model development involved the following three main areas of work. An econometric (time series) analysis was undertaken of Perth's monthly patronage over the last 15 years, using a seasonal difference (‘double-log’) modelling methodology and deriving a set of short run demand elasticities. The model's dependent variable was 'first' boardings (by bus, train) per population per 'standard' month. The main independent variables were average real fare levels, service kilometres, petrol price, employment level and average incomes. Principally as a check on the elasticity estimates derived for Perth, a review was undertaken of the main econometric studies undertaken in the last 10 years of public transport patronage changes in Australasian metropolitan areas and their resultant elasticity estimates.  A surprisingly wide spread of elasticity values was found for each variable in the 10 studies reviewed, although with some signs of estimates clustering around the values expected from wider (international) evidence. In our view, these results indicate not that market responses are very different in different cities; but rather that underlying responses are generally very consistent, and that successful time series modelling is very challenging.  In the light of the above, and based principally on the outputs from the Perth modelling, a 'best estimate' set of elasticity values relevant to Perth was selected. Using these elasticities, a spreadsheet-based forecasting model was formulated, for use by the WA state authorities (principally Transperth/WA Public Transport Authority) to support their short/medium term patronage monitoring, planning and budgeting functions.]]></description>
      <pubDate>Mon, 27 Feb 2017 10:12:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1457959</guid>
    </item>
    <item>
      <title>Parking supply restriction and mode shift at QEII medical centre: a case study</title>
      <link>https://trid.trb.org/View/1326874</link>
      <description><![CDATA[The QEII Medical Centre (QEIIMC) is poised to become the largest hospital complex in Australia. It is a 24/7 employer with a wide range of staff, patient and visitor needs. Due to parking supply constraints, the QEIIMC has undertaken a range of travel demand management measures (TDM) which include parking allocation, paid parking initiatives, green travel incentives, TravelSmart programs and active improvement of sustainable transport modes. This paper discusses the measures employed and their impact on staff travel behaviour, which resulted in car-as-driver mode shares decreasing from 73% to 43% over 4 years. In particular, parking supply constraint was determined to be the driving factor for the observed mode shift. Improvements to public transport services and green travel initiatives assisted in directing mode shift towards specific alternatives, with some applications having more effect than others.]]></description>
      <pubDate>Fri, 10 Oct 2014 09:40:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/1326874</guid>
    </item>
    <item>
      <title>Panel data analysis of public transport patronage growth: an innovative econometric approach</title>
      <link>https://trid.trb.org/View/1286921</link>
      <description><![CDATA[This paper presents an econometric methodology designed to examine, understand and explain patronage growth rates at both the network level and corridor level (i.e. by bus route, bus corridor or train line). This econometric methodology has three distinguishing features: (1) we employ a seasonal difference model (i.e. we focus on explaining patronage growth rates rather than patronage levels); (2) we analyse patronage data that has been disaggregated by corridor, using a corridor-level panel data model; (3) we follow a comprehensive set of stages designed to ensure that any findings are thorough and robust. This econometric methodology has two key benefits. The first is that the methodology assists in isolating and disentangling the contributions of various explanatory variables. The second benefit is that the methodology provides a systematic and scientific means of &lsquo;post-evaluating&rsquo; the pay-offs from network changes and service improvements. This econometric methodology was developed by DMK Consulting and employed in a research project commissioned and supported by the NZ Transport Agency. This research project produced an econometric analysis of public transport patronage growth for a selection of New Zealand cities, including Auckland, Wellington, Hamilton and Tauranga. The primary objective of the research was to examine and explain historical trends in patronage growth and, in doing so, provide up-to-date public transport elasticities. This paper uses a few examples from that research report to demonstrate the benefits of this econometric methodology.]]></description>
      <pubDate>Thu, 23 Jan 2014 09:53:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1286921</guid>
    </item>
    <item>
      <title>Effectively Managing Consumer Fuel Price Driven Transit Demand</title>
      <link>https://trid.trb.org/View/1255616</link>
      <description><![CDATA[This study presents a literature review of transit demand elasticities with respect to gas prices, describes features of a transit service area population that may be more sensitive to fuel prices, identifies where stress points in the family of transit services will emerge, and assembles short- and long-term strategies for transit providers to manage their service when there is volatility in fuel prices.]]></description>
      <pubDate>Tue, 16 Jul 2013 12:23:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/1255616</guid>
    </item>
    <item>
      <title>A longitudinal study on the linkage between public transport demand and land use characteristics: a pseudo panel approach</title>
      <link>https://trid.trb.org/View/1252619</link>
      <description><![CDATA[This study applies a pseudo panel approach to analyse public transport demand in the Sydney Greater Metropolitan Area (SGMA).  A public transport demand model is constructed to incorporate two factors that have been highlighted in the literature of travel behaviour but still under-researched, which are: (i) the temporal effect of demand adjustment; and (ii) the land use characteristics of the built environment.  The research gaps in previous applied pseudo panel data research including estimation techniques and issues involved with the applications to public transport are identified and addressed in this study.  The pseudo panel approach allows for the identification of long-term demand changes using repeated cross-sectional data, which are collected at an individual level with detailed travel-related information and geographical information.  This study constructs static and dynamic pseudo panel data models to analyse public transport demand in terms of its associations with price, socio-economic factors, level of public transport service, and land use factors.  The research findings identify the significant determinants of public transport demand in the SGMA, with a distinction between short-run and long-run demand elasticities.  This suggests a timeframe of 2.13 years is required to reach the long-run demand equilibrium.  The estimated demand elasticities are used to forecast demand for the SGMA with validated results supporting the applicability of the public transport model based on the pseudo panel data.  The main contribution of this thesis is the identification of long-run public transport demand elasticities using a pseudo panel dataset created from existing repeated cross-sectional household travel survey data which uses more individual information than aggregate data.  This approach enables a longitudinal analysis in the absence of genuine panel data, and this in turn provides important implications for urban public transport planning and policy formulation.]]></description>
      <pubDate>Thu, 13 Jun 2013 12:26:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1252619</guid>
    </item>
    <item>
      <title>Econometric models for public transport forecasting</title>
      <link>https://trid.trb.org/View/1249199</link>
      <description><![CDATA[This paper presents the findings from an econometric analysis of public transport patronage growth for a selection of New Zealand cities: Auckland, Wellington, Hamilton and Tauranga.  The primary objective of the econometric analysis was to provide an explanation of historic growth patterns and, in doing so, provide up-to-date public transport elasticities for use by transport planners and policy analysts.  The econometric methods employed differ from conventional approaches because we used panel data models to analyse patronage patterns at a disaggregated level (ie bus route, bus corridor or train line) rather than at a network or city level.  We consider that this approach produces more accurate estimates and demonstrates that statistical methods can be used to &lsquo;post-evaluate&rsquo; the effectiveness of past public transport investments and the impacts of fare increases.  The econometric methodology was developed by DMK Consulting and was designed to ensure that the findings were thoroughly-researched and statistically robust.  The development and implementation of this econometric methodology took from 2009 to 2012 to complete.]]></description>
      <pubDate>Tue, 30 Apr 2013 13:50:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/1249199</guid>
    </item>
    <item>
      <title>The Role of Density in Supporting Sustainable Modes: A New Perspective on the Interaction Between Urban Form and Transit Travel</title>
      <link>https://trid.trb.org/View/1130887</link>
      <description><![CDATA[In recent years, urban policies intended to reduce presumed negative externalities associated with suburbanization have focused on reducing auto travel by manipulating urban form to reduce trip frequencies and travel distances.  In addition, it is assumed that shorter distances provide added opportunities to link more destinations in a single trip chain.  The effectiveness of sustainable transport strategies, however, provides mixed evidence.  This is so because the research is based on ad-hoc empirical specifications, lacking a formal behavioral framework that considers travel the result of activities planned and executed through space and time.  To assess these shortcomings, the authors present an analytical model of the interaction between urban form and the demand for transit travel, in which residential location, transit demand, and the spatial dispersion of non-work activities are endogenously determined.  Theoretically derived hypotheses are empirically tested using a dataset that integrates travel and land-use data.  The authors find that population density does not have a large impact on transit demand and that the effect decreases when residential location is endogenous.  When population density and residential location are jointly endogenous, the elasticity of transit demand with respect to walking distance to a transit station decreases by about 33 percent over the case in which these variables are treated an exogenous. The authors find that households living farther from work use less transit and that trip-chaining behavior explains this finding.  Households living far from work engage in complex trip chains and have, on average, a more dispersed activity space, which requires reliance on more flexible modes of transportation.  Therefore, reducing the spatial allocation of non-work activities and improving transit accessibility at and around subcenters would increase transit demand.  Similar effects can be obtained by increasing the presence of retail locations in proximity to transit-oriented households.  Although focused on transit demand, the framework can be easily generalized to study other forms of travel.]]></description>
      <pubDate>Tue, 26 Jun 2012 09:19:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1130887</guid>
    </item>
    <item>
      <title>Incorporating Crowding into San Francisco Activity-Based Travel Model</title>
      <link>https://trid.trb.org/View/1130861</link>
      <description><![CDATA[Information produced by travel demand models plays a large role in decision making in many metropolitan areas, and San Francisco is no exception. Being a transit first city, one of the most common uses for San Francisco’s travel model SF-CHAMP is to analyze transit demand under various circumstances.  SF-CHAMP v 4.1 (Harold) is able to capture the effects of several aspects of transit provision including headways, stop placement, and travel time.  However, unlike how auto level of service in a user equilibrium traffic assignment is responsive to roadway capacity, SF-CHAMP Harold is unable to capture any benefit related to capacity expansion, crowding’s effect on travel time nor or any of the real-life true capacity limitations. The failure to represent these elements of transit travel has led to significant discrepancies between model estimates and actual ridership. Additionally it does not allow decision-makers to test the effects of policies or investments that increase the capacity of a given transit service. This paper presents the framework adopted into a more recent version of SF-CHAMP (Fury) to represent transit capacity and crowding within the constraints of the authors' current modeling software.]]></description>
      <pubDate>Fri, 22 Jun 2012 13:13:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/1130861</guid>
    </item>
    <item>
      <title>TBEST Model Enhancements - Parcel Level Demographic Data Capabilities and Exploration of Enhanced Trip Attraction Capabilities</title>
      <link>https://trid.trb.org/View/1122333</link>
      <description><![CDATA[FDOT, in pursuit of its role to assist in providing public transportation services in Florida, has made a substantial research investment in a travel demand forecasting tool for public transportation known as Transit Boardings Estimation and Simulation Tool (TBEST).  TBEST incorporates supporting databases that allow users to model transit services for purposes of determining future needs and optimizing current resource deployments by targeting the best markets and route configurations.  This research effort is designed to explore enhancements to TBEST to increase its predictive capability and further enhance its value to transit planners.  Two key and related areas are targeted.  First, the project explores model calibration with parcel-level data.  This involves increasing the geographic precision of transit ridership modeling by using parcel-specific data on land use to understand the activity at the parcel level, and hence, the potential for transit ridership.  Second, the project explores strategies to more robustly address the issue of special generators.    The project determined that transitioning to a parcel-based model is a promising improvement for TBEST.  It enables a more precise capturing of the accessibility of transit stops, which has been shown to be critical to transit use.  In addition, it accommodates a shift to a trip production/attraction-based data framework that enhances the information on which one can base a transit forecast.  In summary, increased computing power, improved databases such as the parcel property inventory, and a strong understanding of factors that influence transit use have enabled the development of more powerful tools to support transit planning.  While transit ridership remains highly variable at the stop level and hence difficult to model, great strides are being made, and the full deployment of parcel-level transit models seems inevitable as a logical advancement in the state of the practice.]]></description>
      <pubDate>Thu, 01 Dec 2011 09:56:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1122333</guid>
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      <title>Bus congestion, optimal infrastructure investment and the choice of a fare collection system in dedicated bus corridors</title>
      <link>https://trid.trb.org/View/1102624</link>
      <description><![CDATA[Microeconomic optimisation of scheduled public transport operations has traditionally focused on finding optimal values for the frequency of service, capacity of vehicles, number of lines and distance between stops. In addition, however, there exist other elements in the system that present a trade-off between the interests of users and operators that have not received attention in the literature, such as the optimal selection of a fare payment system and a designed running speed (i.e., the cruising speed that buses maintain in between two consecutive stops). Alternative fare payment methods (e.g., on-board and off-board, payment by cash, magnetic strip or smart card) have different boarding times and capital costs, with the more efficient systems such as a contactless smart card imposing higher amounts of capital investment. Based on empirical data from several Bus Rapid Transit systems around the world, the authors also find that there is a positive relationship between infrastructure cost per kilometre and commercial speed (including stops), achieved by the buses, which the authors further postulate as a linear relationship between infrastructure investment and running speed. Given this context, the authors develop a microeconomic model for the operation of a bus corridor that minimises total cost (users and operator) and has five decision variables: frequency, capacity of vehicles, station spacing, fare payment system and running speed, thus extending the traditional framework. Congestion, induced by bus frequency, plays an important role in the design of the system, as queues develop behind high demand bus stops when the frequency is high. The authors show that (i) an off-board fare payment system is the most cost effective in the majority of circumstances; (ii) bus congestion results in decreased frequency while fare and bus capacity increase, and (iii) the optimal running speed grows with the logarithm of demand.]]></description>
      <pubDate>Tue, 21 Jun 2011 12:00:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1102624</guid>
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