Transport Demand Models Research Articles

Shared Mobility's Role in Sustainable Mobility: Past, Present, and Future

This article reviews shared mobility, a prominent urban transportation concept with considerable potential to contribute to more sustainable urban mobility. Shared passenger mobility spans diverse services, often leveraging technological advances and disruptions such as smartphones and data analytics to optimize transport resources. Given the broad range of services, a shared mobility taxonomy is proposed, accommodating evolving services. Key challenges for delivering efficient and effective shared mobility services with lower environmental impacts are also identified. Finally, the International Transport Forum transport demand models are used to analyze policy implications and potential effects quantitatively. This article presents a possible scenario for the global evolution of these services to 2050. Results emphasize shared mobility's role in transport decarbonization in the present and future and show that shared mobility may reduce resource use and mobility externalities (e.g., CO2, local pollutants, congestion, urban space use) but that the uptake will differ between Global South and Global North cities.

A COMPARATIVE STUDY OF HEADWAY-BASED AND TRANSPORT SYSTEM-BASED ASSIGNMENTS OF PUBLIC TRANSPORT IN VISUM: THE CITY OF KRYVYI RIH CASE

Many researchers have explored public transport assignment methodologies employing transport modelling software. Nevertheless, there remains a gap in evaluating real-world public transit networks utilizing diverse assignment procedures within Visum software. This paper introduces a thorough comparison of algorithms involved in public transport assignment processes, using the transport model of the city of Kryvyi Rih in Ukraine. The three scenarios of the model were developed depending on the public transport assignment procedure: headway-based, transport system-based utilized to all links, turns, and major turns in the network graph, and transport system-based applied only to the links, turns, and major turns traversed by the active public transport lines. The model of the network comprises 13 transport systems, 7 transport modes, 27598 links, 10097 nodes, 83270 turns, 238 zones, 1748 connections for private transport, 3013 connections for public transport, 534 stops, 1165 stop areas, 1190 stop points, 130 lines and 218 line routes. The transport demand model encompassed 14 demand segments. Compared to the outputs of the model calculation using the headway-based procedure, in the scenarios with transport system-based assignment, passenger flows on rail tram lines significantly decreased. Also, the results of scenarios with transport system-based assignment showed that the passengers extremely use parallel streets for travel from an origin to a destination. The modelling outputs for an actual urban network illustrate that the choice of the shortest route in the transport system-based assignment is closely linked to the main urban roads. This circumstance could potentially result in a rise in the number of transfers needed to sustain accessibility to districts residing far from the central highway.

How to apply the four-step model for 150,000 travel zones: The HIPAT model

Current European transport demand models following the classical four-step approach are less suitable for analysing the traffic volumes on inter-regional road infrastructure with relevant demand of regional trips, as they consider only about 10% of the trip demand with passenger cars. Mainly due to runtime problems, these models are limited to operation at the NUTS-3 level. This level comprises around 1500 regions for Europe with an average diameter of about 50km. However, more than 90% of the trips are shorter than 50km and are, therefore, not adequately considered in the trip distribution model and the trip matrix. Computing this matrix at a higher spatial resolution, such as the LAU-2 level, is not possible as the runtime of the overall model and the size of the trip matrix increase quadratically with the number of travel zones. In the HIPAT model, this obstacle is overcome by an innovative concept operating at different hierarchical levels. This reduces the complexity of the trip matrix, such that the HIPAT model can be applied at large scale for 150,000 travel zones without any runtime problems. This is demonstrated by a prototype implementation for a case study that handles 33,000 travel zones in two minutes. In addition, the limitations of applying NUTS-3-based travel zones for transport modelling are shown: 94.6% of the trips are shorter than the average diameter of the travel zones (50km) and are therefore not correctly represented by the model. Smaller travel zones decrease this number and provide a better basis for analysing infrastructure policies and, ultimately, for decision-making.

PODE: privacy-enhanced distributed federated learning approach for origin-destination estimation

The statewide consumer transportation demand model analyzes consumers’ transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic research, crash databases, and driving simulations have all contributed to our knowledge of how modifications to vehicle design affect road safety. This study proposes an approach named PODE that utilizes federated learning (FL) to train the deep neural network to predict the truck destination state, and in the context of origin-destination (OD) estimation, sensitive individual location information is preserved as the model is trained locally on each device. FL allows the training of our DL model across decentralized devices or servers without exchanging raw data. The primary components of this study are a customized deep neural network based on federated learning, with two clients and a server, and the key preprocessing procedures. We reduce the number of target labels from 51 to 11 for efficient learning. The proposed methodology employs two clients and one-server architecture, where the two clients train their local models using their respective data and send the model updates to the server. The server aggregates the updates and returns the global model to the clients. This architecture helps reduce the server’s computational burden and allows for distributed training. Results reveal that the PODE achieves an accuracy of 93.20% on the server side.

Estimating multinomial logit models with endogenous variables: Control function versus two adapted approaches

It is shown that the control function (CF) method’s estimates of the modal constants in a multinomial logit model (MNL) with endogenous explanatory variables are biased. This has not previously been reported in the literature, and has consequences in demand analysis, transportation policy design and project evaluation. Two adaptations of existing approaches are proposed as alternatives to CF for deriving estimators of parameters in MNL models with endogenous explanatory variables that evidence good consistency properties. The first approach is based on moment conditions while the second one combines parameters obtained in two consecutive estimation stages. Both approaches employ instrumental variables. These two adapted approaches are implemented using simulated data from a transport mode choice problem. The results are compared with those obtained using the classic control-function method, typically used by practitioners for estimating transport demand models with endogenous variables and making quantitative evaluations of transport policies and projects. All three approaches generate similar estimates for the parameters of the explanatory variables, but the two proposed adaptations produce considerably more accurate estimates of the modal constants. This greater accuracy has potentially significant consequences for multinomial logit models’ predictive ability and estimates of marginal effects, elasticities and the social benefits of projects based on consumer surplus calculations.

Introducing a Novel Framework for the Analysis and Assessment of Transport Projects in City Regions

A profound appraisal framework has been developed and refined in transport economics and planning literature for decades, mainly characterised by welfare economic theory, cost–benefit analysis, and transport demand modelling. In summary, the appraisal methodology and its applications have concentrated on single infrastructure measures, marginal impacts identified through ceteris paribus comparisons, forecasts based on trends from the past, and monetary assessments of all quantifiable impacts. However, this framework has been continuously contested in transport planning literature, for instance, for its focus on travel demand and short-term travel time savings. Therefore, we suggest a novel approach for planning and assessing transport schemes in city regions, combining accessibility analyses, quantitative target indicators, and cost-effectiveness analysis. We develop and test this approach by assessing a proposed underground rail project in the Munich city region, the U5 southeast extension. In this case, we define an accessibility target level and estimate the potential for push measures along with the U5 project. We find modest impacts on quantitative targets in the Munich city region: Even when the U5 southeast extension is bundled with push measures in selected transport cells, the contribution to passenger transport-related carbon dioxide emission targets and primary energy consumption targets is low. Nevertheless, we demonstrate that the proposed assessment framework can support strategic transport planning in city regions. We argue for a change in perspective towards supply-side-oriented urban transport planning. Our proposed methodology is a first step in a different direction towards a sustainable mobility planning paradigm.

Data-Driven Framework for Generating Travelers with Demographic-Activity-Travel Information

Transportation demand models and traffic simulations, particularly in the realm of innovative activity-based models, necessitate comprehensive demographic-activity-travel data. Although cell phone data holds the potential to provide detailed insights into activity-travel patterns, transportation planners often resort to aggregate data, constrained by limited socio-demographic attributes because of stringent privacy protection policies. This creates a significant challenge: how to reconstruct detailed traveler profiles from such aggregate data. In this paper, we propose a novel data-driven integration framework, DATG, based on improved Markov and XGBoost classification models, with which we are able to reconstruct travelers with diverse socio-demographic attributes as well as their one-day activity-travel information. We test our model on a real cell phone data set and validate the proposed model by relying on travel survey data. Our framework is able to provide fundamental data support for agent-based models, activity-based models, and transportation planning.

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

A transport model usually consists of a demand model and an available transport network model. The purpose of this article is to identify the key specifics for the development of a regional public transport (PT) demand model and to point out the differences from the urban PT demand model. The traditional four-step transport planning demand model consists of trip generation, trip distribution, modal split, and assignment. This article consists of PT model development, calibration, and validation. A PTV VISUM macroscopic modeling program is used for this research. As a result, this article presents basic suggestions for how a PT demand model should be developed in regions. The presented suggestions for developing a PT demand model can be applied to any low-density region. The rest of the article is structured as follows: (1) Background: presents a literature analysis of the four-step model, modal splits, and the features of the PTV VISUM program; (2) Methods: describes the considered region of Lithuania and the data of the developed model; describes the four-step model, which is adapted to the Lithuanian region; (3) Results: presents the results and main suggestions for creating a PT demand model; and (4) Conclusions: presents the main conclusions of the study.

Quantification and forecasting of regional commercial traffic volumes based on aggregated national data

The increasing volume of traffic in agglomeration areas makes it necessary to take better account of commercial traffic in transport planning and traffic control and to implement new solutions for the sustainable handling of commercial traffic. For this purpose, commercial transport demand models are used, which in turn are attributed with empirical data. However, the determination of key figures of economic traffic in a planning region is very complex and time-consuming. This article therefore presents a method for quantifying and forecasting regional commercial traffic volumes based on aggregated national data. It is applied exemplarily for the Munich Metropolitan Region. The method comprises the extrapolation of national sample data, the updating of outdated national behavioral data, the forecast for a target year and the cutting out of the quantity structure for the planning region. The data used are freely available. They include vehicle diaries, demographic statistics, vehicle registration statistics and employment statistics. Three categories are considered in the data: Economic sectors, vehicle types and settlement patterns. The modelling approach is validated by forecasting historical data and comparing it with verified data. Furthermore, the results are validated by reproducing key figures from independent data sources, whereby the deviations are mostly in the range of 0-10 %.

Increasing the occupancy rates of cars: Carrot, stick or both?

This paper uses the Belgian national transport demand model for an analysis of measures to promote a higher occupancy rate for cars: (a) ‘carrot only’ policies that make carpooling more attractive (b) ‘stick only’ policies that make driving alone more expensive (c) a combination of both. An occupancy rate of 1.5 is obtained with a combination of a road charge of 4 EUR cent per km with a support measure for carpool that reduces the variable generalized costs of carpooling for commuting to work by almost 50%. Overall revenues from the road charge lie around 3 300 million EUR, while the monetary equivalent of the support to carpoolers lies around 1 300 million EUR. As the result of reduced car travel, revenues from fuel taxation decrease by around 700 million EUR. Combining road pricing with targeted support measures for carpooling can improve its public acceptance. However, the policies with the largest impacts on occupancy rates do not pass a cost-benefit test. Existing travel behavior suggests that carpooling has intrinsic drawbacks despite being one of the cheapest travel options, which explains why even measures with far-reaching budgetary implications fail to induce stated policy objectives in Belgium.

Numerical Analysis of Error From Sampling of Alternatives in Logit-Based Demand Forecasting Models with Massive Choice Sets

Use of the multinomial logit (MNL) functional form is widespread in transportation and land use demand modeling. As the state-of-the-art evolves away from aggregate forecasting and toward activity-based models and microsimulation, MNL models are being asked to accommodate increasingly large choice sets. Different strategies exist to address the challenge of estimating models with very large choice sets, but perhaps none is as commonly employed as sampling of alternatives. The effect of sampling of alternatives on parameter estimation has received considerable attention in the scientific literature. Yet comparatively little quantitative research exists that examines the issues that arise when the same sampling strategies are applied in the context of forecasting. In this study we conducted an analysis of the effect of sampling of alternatives in discrete choice models for disaggregate location choice forecasting. First, a novel measure of forecast error was defined and then used to quantify the extent of the problem as a function of sample rate, model error, and size of the universe of alternatives. Finally, we explored the potential for strategic sampling techniques to reduce the error observed under simple random sampling. In general, we found that the proportion of aggregate demand that was misallocated owing to sampling of alternatives was actually reduced as the size of the universe of alternatives increased. Additionally, simple random sampling was shown to outperform importance sampling in most scenarios. These findings suggest that error resulting from random sampling of alternatives is less of a problem for logit-based microsimulation models than previously assumed.

Applied Techniques for Twitter Data Retrieval in an Urban Area: Insight for Trip Production Modeling

This paper presents methods of retrieving Twitter data, both streaming and archive data, using Application Programming Interfaces. Twitter data are a kind of Location Based Social Network Data that, nowadays, is emerging in transportation demand modeling. Data regarding the locations of trip makers represent the most crucial step in the modeling. No research article has specifically addressed this topic with an up-to-date method; hence, this paper aims to refresh methods for retrieving Twitter data that can capture relevant data. The method is unique as the data are gathered for trip production modeling in zonal urban areas. Python script programs were built for both data retrieving methods. The programs were run for streaming data from May 2020 to April 2021 and archive data from 2018. The data were collected within Serang City, which is the nearest provincial city to Jakarta, the capital of Indonesia. In order to gather streaming data with no loss, the program has been run with referencing on sub-district office coordinate locations. Retrieving the intended data produces 1,090,623 documents, of which 54,103 are geotagged data from 2495 users. The study concluded that streaming data produce more geolocation data, while historical data capture more Twitter user data with relatively very little geotagged data and greater textual data than the period covered in this research. Thus, both techniques of retrieving Twitter data for urban personal trip modeling are necessary. Obtaining sufficient data collection using data streaming retrieval resulted in the most effective data preprocessing. This research contributes to Location Based Social Network data mining knowledge, both geolocation and text mining, and is useful for insight into developing trip production modeling in passenger transportation demand modeling using Machine Learning. This study also aims to provide useful methods for transportation system researchers and data scientists in utilizing Location Based Social Network data.

Empowering revealed preference survey with a supplementary stated preference survey: demonstration of willingness-to-pay estimation within a mode choice case

Mode choice models play a pivotal role in transport demand modelling and help transport planners, engineers and researchers with policy and infrastructure investment evaluation. Recent mode choice studies primarily use revealed preference (RP) data to reflect individuals’ true behaviour. However, this may not be the best practice, given the lack of information in RP data. This study uses a nonlinear utility specification for a multinomial logit mode choice model development using high-quality travel data collected by a GPS-based smartphone application complemented by stated preference (SP) data. The model results highlight the impact of sociodemographic variables on mode choice behaviour and individuals’ willingness-to-pay (WTP) when the model is jointly developed compared to stand-alone SP and RP models. The main message of this study is that in addition to collecting RP, which is a reliable and unbiased source of data, collecting complementary SP data is beneficial as it provides information that is not otherwise available in RP data. This may include a proper variation in the public transport cost variable as demonstrated in this study. Moreover, to better understand the travellers' behaviour regarding the trade-off between time and cost a mixed multinomial logit (MMNL) model in the willingness to pay space is developed on the SP data. capturing the unobserved heterogeneity within the estimated WTPs, the MMNL model outputs reveal a higher variation in WTP of car in-vehicle travel time compared to bus in-vehicle travel time.

Approach Draft to Evaluate the Transport System State—A Case Study Regarding the Estimation Ratio Model of Transport Supply and Demand

The article suggests a system dynamics model for estimating the demand for public transport. Traditional scientific and technical transport modeling approaches involve coherent systems, meticulously considering other impactful variables for transport modeling. The vastness of the variables and their combinations hinder us from grasping all possible system interactions. This research aims at proposing a model that comprises decisive factors in relation to the supply and demand in various modes of transport, designing likely scenarios of the transport system development in a specific transport territory. The model uses system dynamics tools to explore the interaction between individual system elements and transport subsystems. A wise choice of crucial system elements, well-adjusted relationships and behavior settings, as well as system dynamics tools, allow for a considerable simplification of an otherwise complex system. The article works with a principle of stock and flow diagrams for forecasting supply and demand in public transport. We take into consideration the implementation of a ‘demand index’ in public and car passenger transport with a subsequent comparison. This innovative approach monitors the development of a regional or municipal transport system while assessing its sustainability. Suggested demand indexes may serve as indicators for a sustainable municipal system. The suggested model reflects data from the South Bohemian region in the Czech Republic and may involve other elements and indicators of a sustainable transport system.

Analyzing indirect economic impacts of wildfire damages on regional economies.

This article estimates the economic impacts of wildfire damage on Korea's regional economies, developing an integrated disaster-economic system for Korea. The system is composed of four modules: an interregional computable general equilibrium (ICGE) model for the eastern mountain area (EMA) and the rest of Korea, a Bayesian wildfire model, a transportation demand model, and a tourist expenditure model. The model has a hierarchical structure, with the ICGE model serving as a core module to link to three other modules. In the impact analysis of a wildfire, three external shocks are injected into the ICGE model: (1) the wildfire damaged area derived from the Bayesian wildfire model, (2) changes in travel times among cities and counties derived from the transportation demand model, and (3) variations in visitors' expenditures derived from the tourist expenditure model. The simulation shows that the gross regional product (GRP) of the EMA would decrease by 0.25% to 0.55% without climate change and by 0.51%-1.23% with climate change. This article contributes to the development of quantitative linkages between macro and micro spatial models in a bottom-up system for the impact analysis of disasters, integrating a regional economic model with a place-based disaster model and the demands of tourism and transportation.

MoPeD meets MITO: a hybrid modeling framework for pedestrian travel demand

Transport demand models were initially designed for simulating car trips. Nowadays researchers and planners are considering pedestrian travel and its health and safety impacts in the regional transport models. However, the existing transport models lack the knowledge and experience in pedestrian modeling for health assessment. This paper contributes to the modeling practice by developing an integrated model called the MITO/MoPeD. The model builds upon previous model development and integrates the fine-grained pedestrian modeling tool into the agent-based transport model. The MITO/MoPeD model is applied to the Munich metropolitan area. Model performances are analyzed based on travel measures (e.g., walk share, trip length distribution, and pedestrian flow) and physical activity volumes. Results show that the MITO/MoPeD model can better represent pedestrian travel behavior than the existing Munich Model. It performed better in simulating the spatial distribution of walk shares and the distribution of walk trip lengths. Moreover, it overcomes the issue of overestimating physical activity volumes. These findings suggest that the MITO/MoPeD model can deliver more precise travel outcomes. More importantly, it is valuable for addressing pedestrian planning issues such as transportation infrastructure investments, land use planning, assessment of safety and health outcomes, and evaluation of environmental impacts.

Fuzzy model of transport demand

The development of transport networks is costly and cannot solve all transport problems. In this regard, scientifically based planning of the development of the transport complex acquires an important role to improve the quality of the functioning of the transport system, such as improving the organization of traffic in various parts of the network, optimizing the system of public transport routes, creating convenient transfer hubs, etc. To solve such problems, the paper considers the development of intelligent transport systems and mathematical modeling of transport demand. The central task of developing a transport model is to determine the equilibrium state of the transport system, i.e., the problem of the distribution of flows in the network, which involves two main stages. In the first stage, one or several correspondence matrices are evaluated based on the traffic flow's initial data and behavior. The second stage consists of the distribution of traffic matrices on the graph of the transport network, i.e., in solving the problem of finding traffic flows or the problem of transport equilibrium. The problem of transport equilibrium is reduced to an optimization problem in which restrictions are imposed depending on the values of the elements of the traffic matrix. The search for new methods for solving optimization problems of weakly formalized processes is caused by the presence of processes and phenomena, which is difficult to describe by methods of classical mathematics due to their indeterminacy and unreliability, incompleteness, and fuzziness of the initial information. The mathematical model of the optimization problem is described by the objective function and constraints. Classical mathematical programming and its variations are largely a normative methodology for effective choice. Fuzzy programming highlights the natural multiplicity of inaccurately defined goals, restrictions, and their meanings.

Decarbonizing the German transport sector: Why the 2030 sector targets are likely to be missed

In order to meet its 2030 climate sector target, the German transport sector must experience a trend reversal and reduce its greenhouse gas emissions drastically. Envisioned technological solutions vary between different modes of transport and technological readiness. By making use of an integrated model chain we examine possible short-run emission reductions for different technology pathways. Through fleet and transport demand modeling we provide final energy demand and CO2 emissions of the sector at five-year intervals. Modeling results show short-run emission reductions mainly in passenger car traffic. However, all technology pathways overshoot the 2030 sector target considerably. Consequently, ambitious measures that induce an avoidance and shifting of trips must complement the rapid ramp up of electric powertrains to meet the sector target.

Boosting intermodal rail for decarbonizing freight transport on Java, Indonesia: A model-based policy impact assessment

This paper assesses the impact of a modal shift action program aimed at reducing CO2 emissions from surface freight transport on Java - Indonesia's center of economy and population. The action program combines fiscal measures and infrastructure development strategies which are designed through consultation with policy makers and relevant stakeholders. I test three ambition levels of the action program -basic, moderate, strong on CO2 emissions, using a 4-step freight transport demand model and a fleet emission model. For each of the ambition levels, I estimate the shift from road to rail freight and savings in CO2 emissions attained. I found that a significant transport demand could be shifted from road to rail where rail freight share could reach up to 20% of the total cargo transported by 2030. This shift to rail will also lead to a considerable reduction in the total annual CO2 emissions, up to 21.6% under the strong ambition scenario. In terms of economic benefits, the shift will also reduce total transport costs up to 7.6% under the most ambitious scenario.The high effectiveness of the measures to abate emissions substantiates the need for a comprehensive cost-benefit analysis to further assess the cost-effectiveness of the action program. Based on the modelling, several following key policy measures are identified to improve the share of rail freight: (1) improving service quality of rail freight, (2) building sufficient rail infrastructure capacity, and (3) setting up dedicated funding program for intermodal rail infrastructure.

Deriving transport appraisal values from emerging revealed preference data

Transport demand models are widely used to inform policy making and produce forecasts of future demand. A core output derived from demand models is the Value of Travel Time (VTT), which provides insights on the trade-offs that travellers are willing to make in terms of travel time and travel cost. VTT estimates are a critical input to cost-benefit analyses and feasibility assessments of potential projects and thus play a crucial role in transport planning and policy decisions. While much of the early work on VTT made use of revealed preference (RP) data, their use decreased due to growing concerns about reporting errors that may result in omitted observations and measurement errors in the model inputs. As a consequence, VTT measures have, for the last two decades, primarily been estimated using state-preference (SP) surveys. While SP methods can assess the individual trade-offs in a controlled manner, they are prone to behavioural incongruence. More recently, RP data from passively-collected data sources have raised the promise of accounting for some of the limitations of traditional RP surveys due to the minimal (or even no) active input from the respondent. The present study utilises such a dataset that combined a 2-week trip diary captured through smartphone GPS tracking with a household survey containing individual socio-demographic information. A mixed Logit model for mode choice was specified and the estimated parameters were then applied on the National Travel Survey to calculate the VTT estimates. Those estimates were further adjusted based on trip distances to get more representative national VTT values. This process resulted in estimates similar to the official UK guidelines used in transport appraisal that were obtained from SP data, where our results are not affected by concerns about response quality or survey artefacts. The findings hence strengthen the case for shifting towards passively generated RP data sources and are important for transport practitioners.