Transport Demand Models Research Articles

Transit Ridership Modeling at the Bus Stop Level: Comparison of Approaches Focusing on Count and Spatially Dependent Data

Boarding and alighting modeling at the bus stop level is an important tool for operational planning of public transport systems, in addition to contributing to transit-oriented development. The interest variables, in this case, present two particularities that strongly influence the performance of proposed estimates: they demonstrate spatial dependence and are count data. Moreover, in most cases, these data are not easy to collect. Thus, the present study proposes a comparison of approaches for transit ridership modeling at the bus stop level, applying linear, Poisson, Geographically Weighted and Geographically Weighted Poisson (GWPR) regressions, as well as Universal Kriging (UK), to the boarding and alighting data along a bus line in the city of São Paulo, Brazil. The results from goodness-of-fit measures confirmed the assumption that adding asymmetry and spatial autocorrelation, isolated and together, to the transportation demand modeling, contributes to a gradual improvement in the estimates, highlighting the GWPR and UK spatial estimation techniques. Moreover, the spatially varying relationships between the variables of interest (boardings and alightings) and their predictors (land use and transport system features around the bus stops), shown in the present study, may support land use policies toward transit-oriented development. In addition, by using an approach with little information, the good results achieved proved that satisfactory boarding and alighting modeling can be done in regions where there is a lack of travel demand data, as in the case of emerging countries.

Estimating global demand for land-based transportation services using the shared socioeconomic pathways scenario framework

The global demand for transportation is growing owing to accelerated socioeconomic development worldwide. If the current modes of transportation, consisting mostly of personal internal combustion engine vehicles, dominate this growth, greenhouse gas emissions will rise and worsen the climate crisis. A key empirical challenge in understanding the barriers and opportunities for low-carbon transportation systems in developing countries is the lack of demand data. Because existing country-specific transport demand models focus on countries with robust historical datasets, it has been difficult to estimate the service demand for developing countries. To address this limitation, we develop a log–log regression model linking socioeconomic variables with demand for land-based passenger and freight transport services. Using socioeconomic data from the shared socioeconomic pathways (SSPs) developed for climate analysis, we then produce scenario-based estimates for land-based transportation services for 179 countries around the world. The global average annual land-based passenger demand growth rate ranges between 1.3% and 4.1%, while the annual growth rate for land-based freight demand ranges between 3.1% and 3.6% across the 30 years between 2020 and 2050. Middle-income countries in Asia such as India and China, show the highest expected transport demand across all scenarios. Meanwhile, the results suggest that low-income countries in the sub-Saharan African region are likely to experience the largest growth in demand for passenger and freight transport services. These two trends come together at an inflection point around the year 2030. Prior to 2030, the transport demand was the highest in East Asia. After 2030, there is an ascendancy in transport demand in South Asia and sub-Saharan Africa, whereby the cumulative demand share of these two regions reaches near parity with that of East Asia by 2050. Sustainably meeting this growing demand will require the adoption of data-driven transport planning tools and leveraging cross-linkages across other energy sectors such as electricity.

Pathways to Carbon-Free Transport in Germany until 2050

The transport sector has to be widely decarbonized by 2050 to reach the targets of the Paris Agreement. This can be performed with different drive trains and energy carriers. This paper explored four pathways to a carbon-free transport sector in Germany in 2050 with foci on electricity, hydrogen, synthetic methane, or liquid synthetic fuels. We used a transport demand model for future vehicle use and a simulation model for the determination of alternative fuel vehicle market shares. We found a large share of electric vehicles in all scenarios, even in the scenarios with a focus on other fuels. In all scenarios, the final energy consumption decreased significantly, most strongly when the focus was on electricity and almost one-third lower in primary energy demand compared with the other scenarios. A further decrease of energy demand is possible with an even faster adoption of electric vehicles, yet fuel cost then has to be even higher or electricity prices lower.

Framework for designing sample travel surveys for transport demand modelling in cities: some comments

Framework for designing sample travel surveys for transport demand modelling in cities: some comments

Seismic Resilience Assessment Strategy for Social and Sustainability Impact Evaluation on Transportation Road Network: A Seismic Liquefaction-Induced Damage Application

Transport networks play a critical role for living communities, as they facilitate the exchange of people and goods and foster economic growth. Improving their resilience against seismic hazards, among which liquefaction is by far one of the most significant and complex, is consistent with most of the Sustainable Development Goals pinpointed by the United Nations’ Agenda. In this paper, an original methodological framework, combining innovative Geo-statistical approaches to analyze soil properties, prediction models for soil liquefaction, and calibrated transport demand models providing the social and economic cost associated with seismic-induced road damages and closures within a renewed Geographical Information Systems (GIS) workspace, is proposed. In particular, based on traditional risk assessment evaluation, an innovative approach to evaluate the exposure in terms of economic loss due to lack of accessibility is presented. The methodology is applied to a district area in northern Italy that underwent a recent seismic event that caused several soil liquefaction phenomena. Results provided by a sensitivity analysis on a stochastic (return period) basis are derived: as the seismic intensity increases, the total social costs increase, but the trend of the rates due to traffic delays and the loss of accessibility are irregular. Although further simulation scenarios need to be undertaken, the proposed methodology seems to provide an effective planning tool to evaluate preventive strategies aimed at improving the resilience of transport networks against liquefaction risk.

Compatibility between delay functions and highway capacity manual on Iraqi highways

Abstract Volume delay functions (VDFs) are mathematical relationships used by the traffic allocation step of demand forecasting models to take into account the effect of increased traffic flow on the time spent to travel each possible route between different travel sources and destinations. The VDF is usually applied in static traffic assignment to describe the resultant link travel times, as a function of flow and capacity and free-flow travel time. This study aims to investigate the interface between the delay functions used by demand forecasting models and the highway capacity manual (HCM) model flow-speed relationships. The most commonly used VDFs in transport demand modeling packages in the literature were identified. The Bureau of Public Roads (BPR), conical functions (CF), Akçik and Troutbeck function (ATF), and delay logistics function (LF) were described. The four VDFs and the current HCM models were calibrated for the Iraqi road environment, and their compatibilities were examined. Results show that the best adjustments were obtained using the BPR function (quadratic error 0–0.012) and LF (quadratic error 0–0.002). The roles of CF and ATF were used with care, as both appear to neglect the delay in the condition of small to medium traffic patterns typical to country roads. Particularly, in response to single-lane roads, the LF has proven to be useful due to its potential to represent significant delays for low traffic flows and simultaneously produce more delays in congestion conditions; furthermore, the effect of flow as well as intersection spacing is obviously nonlinear. As flow reaches 600 pcu/h/lane, running time increases quickly. With more intersections per kilometer, the impact is obviously greater.

Modeling cyclist behavior using entropy and GPS data

This paper analyzes the relation between Shannon’s information entropy and trips made by cyclists in an urban context. Such entropy indicator depends on people distribution across a territory in a specific time. Its evaluation and temporal evolution have multiple applications in transport planning, from the specification of demand distribution models to the calibration and validation of transport demand models to be used where transport data are scarce. This feature is crucial in transport planning, as the lack of robust data to estimate travel demand is still a common challenge, especially for cycling mobility. The entropy indicator has been applied to the cyclists’ case in the Italian city of Bologna, using approximately 165,000 GPS traces recorded from April to September 2017. The results are encouraging because the entropy indicator is able to explain the evolution of the agents’ distribution throughout a day across the territory. However, it is suggested that future research may further deepen the concept to test its transferability, and to better establish its sensibility to other case studies.

Mode Choice Effects on Bike Sharing Systems

Bike-sharing systems (BSS) are offered in many cities and urban municipalities and urban areas without such systems are thinking about their introduction. In addition, many studies on BSS are available; however, neither mode nor route choice parameters are available for station-based BSS, which are required for the implementation of BSS in local and regional transport demand models. As a result, this makes it impossible to simulate demand model-based effects of these systems on other transport modes and e.g., calculate scenario-guided modal shifts. The paper presents results obtained from a survey study, which aims to estimate BSS-related choice parameters. The study combined computer-assisted telephone interviews (CATI) for a collection of revealed preferences (RP) on the use of BSS with a follow-up paper-and-pencil survey on stated preferences (SP) of 220 BSS users and non-users from the Rhine-Neckar area in mid-west Germany. Considering the three transport modes BSS, public transport (PT), and private motorized transport (PMT), results from this choice experiment and, according to behavioural parameters, allow integration of BSS in transport demand models and a simulation of modal shifts. Survey design, mode-choice experiment, and choice models are presented in this paper.

Computational Graph-Based Mathematical Programming Reformulation for Integrated Transport Demand and Supply Models

Computational Graph-Based Mathematical Programming Reformulation for Integrated Transport Demand and Supply Models

Building latent segments of goods to improve shipment size modelling: Confirmatory evidence from France

Freight transport demand models are generally based on administrative commodity type segmentation which are usually not tailored to behavioral freight transport demand modelling. Recent literature has explored new approaches to segment freight transport demand, notably based on latent class analysis, with promising results. In particular, empirical evidence from road freight transport modelling in Germany hints at the importance of conditioning and handling constraints as a sound basis for segmentation. However, this literature is currently sparse and based on small samples. Before it can be accepted that conditioning should be integrated in the state-of-the-art doctrine of freight data collection and model specification, more evidence is required. The objective of this article is to contribute to the issue. Using detailed data on shipments transported in France, a model of choice of shipment size with latent classes is estimated. The choice of shipment size is modelled as a process of total logistic cost minimization. Latent class analysis leverages the wide range of variables available in the dataset, to provide five categories of shipments which are both contrasted, internally homogenous, and directly usable to update freight collection protocols. The groups are: "‘standard temperature-controlled food products"’, "‘special transports"’, "‘bulk cargo"’, "‘miscellaneous standard cargo in bags"’, "‘palletised standard cargo"’. This segmentation is highly consistent with the empirical evidence from Germany and also leads to better estimates of shipment size choices than administrative segmentation. As a conclusion, the finding that conditioning and handling information is essential to understanding and modelling freight transport can be regarded as more robust.

Новий метод реалізації інтервальної концепції моделювання транспортного попиту

The article examines the peculiarities of the OD-matrix generation within the framework of the interval concept of transport demand modeling, which consist in the absence of guarantees of successful distribution of known departure and arrival capacities of transport districts of the transportation object among the matrix cells, in the presence of additional, to capacities, restrictions. The possibilities of full distribution of demand on the OD-matrix in the presence of such restrictions are analyzed and two methods to overcome the existing problem are proposed. For a small number of restrictions, a two-stage matrix filling algorithm is formed. The first stage consists in the usual distribution of transport demand volumes using a random number generator, the second is in the redistribution to open cells of those values that fell into closed matrix cells using closed cycles known from the transport task. For a significant number of restrictions that arise when modeling matrices of route correspondences, or in other cases in which the modeling of transport demand using closed loops becomes impossible, has been created a new method of implementing the interval concept of demand modeling. It is based on repeated use of the basic method of distribution of transport demand without restrictions, which always leads to a complete distribution of capacities according to the OD-matrix. Restrictions imposed on the matrix are taken into account by removing from its current instance those values that are subject to restrictions. The deleted values form the conditions for the generation of the next instance of the matrix until all the departure and arrival capacities of the transport districts are distributed among such matrices. The final matrix is the sum of all the intermediate matrices created in this way. This method of forming OD-matrices allows guarantying a positive result of the generation of random matrices with restrictions and to ensure the full implementation of the interval concept of transport demand modeling.

Proposed Public Transport Demand Model for Developing Route Plan in Lipa City, Batangas

Proposed Public Transport Demand Model for Developing Route Plan in Lipa City, Batangas

Theory for socio-demographic enrichment performance using the inverse discrete choice modelling approach

In light of the growing availability of big data sources and the essential role of socio-demographic information in travel behaviour and transport demand modelling more broadly, the enrichment of socio-demographic attributes for anonymous big datasets is a key issue that continues to be explored. The common shortcoming of existing socio-demographic enrichment approaches concerns their lack of consistent theory that can link their enrichment performance (i.e. the ability to correctly enrich the required attribute) to the underlying covariance structure in the anonymous big datasets. In other words, existing approaches are unable to indicate, prior to the enrichment, to what extent it will be successful. Instead, they require undertaking the enrichment itself to assess and validate it post factum, incurring the effort and cost of the activity. An alternative and arguably preferable way would be to have a prior indicator as to whether an enrichment is likely to be sufficiently effective for the desired application.Towards this end, this paper draws upon the Inverse Discrete Choice Modelling (IDCM) approach to demonstrate what is termed as the IDCM performance theory, which systematically and in a tractable manner links the socio-demographic enrichment performance of the IDCM approach to the structure of the underlying datasets. This is achieved by recalibration of the constant, a technique adopted from conventional discrete choice modelling practice, while also drawing upon information theory employed in the context of communication systems. The established IDCM performance theory is validated in two empirical applications where performance of the IDCM approach in enriching several socio-demographic attributes, given travel behaviour patterns, is successfully estimated. Additionally, the IDCM approach is found to perform comparably to commonly used methods in previous socio-demographic enrichment efforts. It is thus argued that the capability of the IDCM performance theory to predict and explain its enrichment performance under different data conditions can facilitate informed and transparent transferability of the IDCM framework in the socio-demographic enrichment for anonymous big datasets.

Mobility in Transportation Surveys

An attempt to identify key factors impacting trip generation in different size cities is presented in this paper. Mobility is the fundamental factor in transport demand models, both for the present state and those for forecast scenarios. Moreover, research on inhabitants' mobility plays an important role in the process of urban and rural traffic modelling. It comes from the fact, regular transport surveys make it easier to determine factors impacting the number of trips taken by inhabitants in any selected study area, and to determine trends in these factors. Presentation of example results of mobility rate based on transportation surveys taken in various areas of Poland is the main goal of this paper. Results of analyses of selected macro-economic factors in transportation systems shaping are presented either. The trends in the number of inhabitants and their age composition, and motorization rates are those factors. They impact mobility ratios in the study area. Values of mobility ratio were developed based on the results of surveys which have been done in Poland till now, and on the authors' research. Mathematical regression models in estimating mobility ratio versus the selected independent factors are the results of this research either.

The Demand Model of App-Based Transportation Household Scale in Semarang, Indonesia

In the development of transportation systems, Application-Based Transportation is an innovation to adapt to technological advances. It offers users an alternative mode that is cheap, easy, and flexible according to their needs. The Application-based travel demands can be seen from its socio-economic and travel characteristics. In Indonesia, previous studies have concentrated on study areas on a city scale, with diverse land use classifications not only on the residential area which is the most users of app-based transportation. The modelling results in this study were obtained from spatial simulation and partial linear regression analysis with the T-test as the final stage of analysis. As a result, demand for App-Based Transportation is affected by two factors which include age and travel costs. They are inversely proportional to the frequency of travel. In this case, this mode is mostly used by the population with young age and low cost of travel. Also, this mode is only used on short-distance trips and trips during rush hour in the morning. During the afternoon rush hour, the trip is transferred to public transportation, which has a lower cost. Therefore, this study aims to determine the application-based transportation demand model for household units in Tlogosari, Semarang, Indonesia. An additional in-depth study is needed to be carried out on the degree of motorcycle safety to improve services.

Optimal pricing of car use in a small city: A case study of Uppsala

Studies of cities that have successfully shifted demand from cars to more sustainable modes suggest that coordinated packages of mutually reinforcing policy instruments are needed. Congestion charges and parking fees can be important parts of such packages. This paper examines the introduction of welfare-optimal congestion charges and parking fees in a model calibrated to Uppsala, a small city in Sweden. These effects are modeled with a simple transport demand model for the welfare optimization of parking fees, congestion charges, and public transport provision. The results suggest that welfare-optimal congestion charges in Uppsala are as high as EUR 2.8 in peak hours and EUR 1.4 in off-peak hours. A rough cost–benefit analysis shows that the introduction of congestion charges in Uppsala is welfare improving if the operating costs of congestion charges are proportional to city population size. In the main scenarios, optimal congestion charges and parking fees reduce the number of car trips by 10% and 7%, respectively. The model can be used to assess when it is worthwhile to introduce congestion pricing.

Exploring the future energy-mobility nexus: The transportation energy & mobility pathway options (TEMPO) model

• Document the approaches and methods used in the TEMPO model. • All-inclusive passenger and freight transport demand model that covers the entire U.S. • Generate scenarios of technology adoption and future energy use and emissions. • Innovative household-level representation of passenger mobility and choices. • Comparison with the EIA AEO for a business-as-usual scenario. This paper documents the approaches and methods used in the Transportation Energy & Mobility Pathway Options™ (TEMPO) model to evaluate passenger and freight demand for transportation and mobility services, project vehicle ownership and technology adoption decisions, and determine transport mode choices to derive scenarios of future energy use and emissions. TEMPO is an all-inclusive transportation demand model that covers the entire United States, with an implicit spatial resolution and an hourly temporal resolution that allows for generating time-resolved energy use profiles to assess multisectoral integration aspects. Key features of the TEMPO model include the ability to perform endogenous out-of-sample forecasting to extrapolate recent emerging trends and analyze impacts of disruptive technological breakthroughs and behavioral changes. TEMPO employs an innovative representation of passenger mobility demand stemming from household-level decisions that determine vehicle adoption, ownership, and use based on sociodemographics (e.g., income, household composition), technology attributes (e.g., travel cost, time), geography (e.g., urban, suburban, rural) and population-specific multiday mobility and travel requirements. This representation enables a more forward-looking perspective on the use of new mobility options and the adoption of alternative fuel vehicles, as well as a more accurate representation of their energy usage profiles than previous modeling approaches. A comparison with the U.S. Energy Information Administration’s Annual Energy Outlook showcases the ability of TEMPO to accurately replicate widely accepted projections by representing the key elements of the entire transportation sector at the appropriate level of resolution. TEMPO is intended to generate future scenarios of technology adoption, energy use, and emissions in the transportation sector to compare alternatives, inform decision makers, and assess integration with energy infrastructure and supply systems at an appropriate spatiotemporal resolution.

Exploring spatial interaction effects in freight trip generation model for intermediate and pure receiver establishment

Freight trip generation (FTG) plays an essential role in urban planning and traffic assessments for developing long-term transportation demand modeling. This study explored the spatial interaction effects on the development of FTG models for intermediate and pure receiver establishment using spatial econometric techniques. FTG models are developed by considering three types of spatial interaction effects such as exogenous, endogenous, and residual spatial autocorrelation effects. Initially, the supply chain variables and demographic characteristics are collected in Tiruchirappalli city, Tamil Nadu, India. The spatial indicators for intermediate and pure receiver establishment are identified by estimating spatial autocorrection of the variables using Moran’s I method. Further, the locational information of the study area is incorporated for representing the spatial neighborhood structure in the spatial weight matrix. The spatial interaction effects are assessed using spatial econometric modeling techniques. FTG models are developed using the linear Ordinary Least Square (OLS) technique and Spatial Econometric techniques such as Spatially Lagged X model (SLX), Spatial Durbin Model (SDM), and Spatial Durbin Error model (SDEM). The developed models are compared using the Bayesian approach, and the suitable FTG model is identified to explain the spatial interaction effects in more precisely. The results show that the SDEM is the most suitable FTG model than other spatial econometric models for both intermediate and pure receiver establishment. Spatial indicators such as own effects and spillover effects associated with the SDEM are estimated and interpreted for explaining the freight trip activity. Moreover, local spatial spillover significantly recognizes the urban fright trip generation for both intermediate and pure receiver establishment.

Air quality improvement via modal shift: Assessment of rail-water-port integrated system planning in Shenzhen, China

The escalating concerns regarding air pollution problems surrounding port cities have attracted much research attention. The Port of Shenzhen is one of the busiest container ports worldwide, only third to Shanghai and Singapore globally. However, 70% of the freight transportation demand is satisfied via on-road trucks, leading to serious traffic congestion, road accidents and air pollution issues in the city of Shenzhen. This study aims to assess the environmental benefits of modal shift of port-connecting freight transportation by increasing the use of rail and waterborne systems in Shenzhen. To evaluate the environmental benefits of the multimodal transportation strategy in 2025, we employed traffic datasets with a high spatial resolution and a transportation demand model to establish emission inventories and applied them in air quality simulations. Our results indicate that the implementation of multimodal transportation systems could notably reduce the truck volume along major freight corridors, except for roads adjacent to the planned inland ports. The freight traffic activities along the major freight corridors are reduced by nearly 70% over the original freight volume, resulting in a drastic reduction in the emission intensity. Under the most progressive policy-enhanced strategy (PPP) scenario, the total well-to-wheel (WTW) NOX, fine particulate matter (PM2.5) and CO2 emissions could be reduced by 8881 t, 104.8 t and 688 × 103 t, respectively. The NO2 concentration in traffic-intensive areas could be reduced by 5 μg/m3, and the 8-h maximum O3 concentration could be reduced by 0.34 μg/m3 on the average (up to 1.1 μg/m3 in certain areas). Our research indicates that a shift from traditional road transport to cleaner railway and waterway transport could deliver transportation and environmental benefits to port cities.

Deployment of electric vehicles in China to meet the carbon neutral target by 2060: Provincial disparities in energy systems, CO2 emissions, and cost effectiveness

Switching to electricity in the ground transport sector is considered a promising way to achieve the energy transition and CO2 emission reductions required to meet China's carbon neutral target by 2060. In this study, a transport energy model containing an elaborate transport demand model and a technology bottom-up model for detailed behavioral and technological representations was developed to investigate how electric vehicles (EVs) will penetrate the markets in the long-term and what impacts on energy consumption and emissions would emerge following EV adoption in China at the provincial level. A set of scenarios was created based on different policy interventions for the promotion of electric mobility. The results showed that subsidies for EV adoption would significantly boost the market share and foster a rapid transition away from fossil fuels, while the business-as-usual scenario would only generate a moderate influence on EV penetration. The regional differences in the emission reduction potential due to EV subsidies across the 31 provinces indicated that policy instruments for EV promotion would have significant positive effects in the developed provinces in both the capital metropolitan area and southeastern China. An economic cost analysis revealed a relatively low economic feasibility in northeastern and northwestern regions where the emission reduction potential is also lower than the national average, implying that the developing provinces in northeastern and northwestern China require greater financial assistance and the establishment of supportive policies for EV promotion.