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This study examined the sustainability of shared micromobility services using data from 48 cities in the US using a 21-day smartphone travel diary and survey data. Population-weighted analysis indicated a much smaller share of transit connection than in prior reported studies, with more reliable data. However methodological decisions could be a cause for such discrepancies suggesting a sensitivity analysis of this same data may be a good next research step. Results also indicated median VMT reduced per micromobility trip to be roughly 0.15 miles for e-scooter share trips and 0.25 miles for bike share (including e-bike) trips. Models of mode substitution confirm prior evidence of factors affecting car substitution including trip distance as the strongest factor. This study also proposed two frameworks for building a sketch planning tool for examining VMT reduction from future micromobility services. This tool could help cities and regions better plan for the micromobility services to achieve real VMT and GHG reduction goals. While more research is needed to employ this framework, it helps motivate a series of additional research topics to inform a decision support tool for shared micromobility planning. View the NCST Project Webpage

We present a new model for estimating the distribution of international freight transport over transport modes that is directly applicable to aggregated data, allows estimations and predictions also when some modes are infeasible on some routes, and requires few processing time and memory space. It builds on the assumption that demand for transport by a given mode is driven by trading firms' and consumers' preferences about saving transport costs and time. In contrast to conventional mode-choice models, it is linear and grounded on consumer demand theory. Applying the model to international freight transport as recorded in the latest upgrade of UN Comtrade reveals an average cost elasticity of transport demand of − 0.32 and an average time elasticity of − 0.18. In addition, we find significant independent mode-specific effects. Cost and time elasticities are highly dependent on the type of commodity transported. The cost elasticity ranges from zero to − 1.9 and the time elasticity from zero to − 3.3 across commodity groups defined at the four-digit level of the Harmonized System classification. These findings suggest that policy measures, exogenous shocks or other events that change the relative transport costs and transit times across modes can cause modal shifts—for some commodities more than for others—thereby mitigating the loss in welfare. [ABSTRACT FROM AUTHOR]

Mobility is one of the most debated issues in the current discussion on transport policies and the climate change. Related indicators are the modal split, transport growth and travel time savings, used for travel behaviour evaluations. In official transport statistics, the modal split is generally used in two ways, according to the number of modal trips and to the modal trip length. Although they are usually used separately, we will show that they are directly connected, where the latter is a weighted version of the former. The introduction of the reference units of "person" and "day" overcomes the limitations of a trip-based definition and extends the ansatz to a mobility-based methodology, where all modal combinations can be considered. Empirical data analysis shows, that transport growth is founded on the increase of individual motorised transport at the disadvantage of pedestrians, whereas other modes remain fairly stable. Daily travel time generally increases especially with individual motorised transport in comparison to non-motorised modes, which shows an inconsistency, for example, for some assumptions or approaches of travel time savings. The proposed methodology does not require any prior assumptions and provides therefore a simple and general applicability. The data analysis is based on national travel survey data from Germany, Switzerland, UK and USA from the 1970-ies up to 2018. One main result of the paper is a frame of reference for the relativity of mobility behaviour, which consistently connects these mobility indicators. • Using modal split definition and developed new functions for mobility behaviour. • Introducing reference units of person and time for a framework for mobility. • The frame of reference provides the basis for a new definition of mobility behaviour. • Further extensions are introduced to transport growth and travel time savings. • A systematic consistency and complementarity is obtained between the mobility indicators. [ABSTRACT FROM AUTHOR]

One of the ways to improve energy efficiency in transportation is through efforts aimed at increasing the usage of public transportation by residents. This, in turn, is closely related to residents' preferences. One of the most frequently cited factors influencing the attractiveness and quality of public transportation is the frequency of connections. This is important not only for urban transport but also regional transport, which has significantly lower passenger flows. This paper aims to present how the frequency of connections affects the attractiveness of regional transport. An original method for determining the attractiveness of public transport, based on the share of adult, senior, and youth passengers with single tickets and monthly passes, is introduced. The results of research on the structure of passengers and flows to/from the district center are presented. Based on the research results, attractiveness factors are calculated for each town. The statistical analysis clearly indicates there is a strong correlation between the attractiveness of regional transport and the frequency of connections (Rs = 0.807, p = 0.001). Moreover, for every connection increase, the number of adult passengers will increase by an average of 1.5. Assuming that these additional passengers switch from individual to public transportation, the resulting reduction in energy consumption due to an increase of one connection is 0.33–0.69 kWh for each kilometer traveled by these passengers. [ABSTRACT FROM AUTHOR]

Abstract We present a new model for estimating the distribution of international freight transport over transport modes that is directly applicable to aggregated data, allows estimations and predictions also when some modes are infeasible on some routes, and requires few processing time and memory space. It builds on the assumption that demand for transport by a given mode is driven by trading firms’ and consumers’ preferences about saving transport costs and time. In contrast to conventional mode-choice models, it is linear and grounded on consumer demand theory. Applying the model to international freight transport as recorded in the latest upgrade of UN Comtrade reveals an average cost elasticity of transport demand of − 0.32 and an average time elasticity of − 0.18. In addition, we find significant independent mode-specific effects. Cost and time elasticities are highly dependent on the type of commodity transported. The cost elasticity ranges from zero to − 1.9 and the time elasticity from zero to − 3.3 across commodity groups defined at the four-digit level of the Harmonized System classification. These findings suggest that policy measures, exogenous shocks or other events that change the relative transport costs and transit times across modes can cause modal shifts—for some commodities more than for others—thereby mitigating the loss in welfare.

This study explores the factors associated with observed changes in transportation mode shares over the period from 2012 to 2017 (corresponding with the period between the two most recent household travel surveys conducted in California). In contrast with the goals of the California Department of Transportation and the State Transportation agency, walking, biking, and using transit all decreased during this period, and driving and the use of personal vehicles increased. There are a number of factors typically associated with transportation mode choices, including socio-demographics, attitudes, life stages, land use and infrastructure availability. Further, large scale events may also have an effect on travel trends; for example, the Great Recessionmay have impacted individuals’ ability to own a personal vehicle and therefore increased the use of alternative means of transportation during the years leading up to our survey period. Similarly, the 2013 passage of legislation allowing for non-citizens to obtain a driver’s license in the state of California, may have impacted mode shares over the study period. This paper compares these and other factors impacting mode shares in 2012 and in 2017 to answer part of the question about why we see this decrease in the use of active modes over this period and what types of planning, programs, and policy actions may help to reverse this trend and get California back on track to increase walking, biking and the use of public transit.View the NCST Project Webpage

Several determinants affect the reason to cycle or not, and some of them are described in a detailed way in the current technical literature review. The recent spread of new modes of active mobility brings up questions for urban transport planners on how to foresee future demand and assess safety conditions; from this comes the need to explore the relationships among several determinants. In this paper, after the collection of the main data required, three Regression Models are proposed, which demonstrate evidence for the role of safety and energy expenditure issues as important predictors. The method is applied to a dataset of 90 Italian cities selected according to their class of dimensionality and geographical position. The three models for each class of dimensionality (50,000–100,000 no. of inhabitants, 10,000–50,000 no. of inhabitants, and 0–10,000 no. of inhabitants) show a good accuracy (in terms of adj-R2 values of 0.6991, 0.7111, and 0.6619, respectively). The results show that energy expenditure, which is related to the terrain characteristics of an urban area and individual aerobic abilities, and safety perception, which is related to cycle network extensions, appear to be significant determinants in predicting bicycle modal share. The aim is to provide a useful and simplified tool, when only aggregated-type data are available, to help urban road designers and city planners in identifying and forecasting bike-sharing. [ABSTRACT FROM AUTHOR]

This paper proposes a vibration model for modal analysis and vibration control of a vertical cable-dual tuned mass damper (C-DTMD) coupled system. The proposed model introduces several non-dimensional parameters to describe the in-plane vibration of the coupled system. Moreover, the phenomenon of modal split is comprehensively studied to present the complex vibration modes. Parameter analysis is conducted to show the influence of length ratio, mass ratio, damping ratio, and frequency ratio on vibration reduction. In addition, the theoretical model is verified using finite element methods. A practical vertical cable in Jiangyin Bridge with designed wind loads is chosen as a numerical example. The results show that DTMD devices can effectively reduce the modal vibration and have great potential for multi-mode vibration control of vertical cables in suspension bridges. The proposed model can accurately describe the in-plane vibration modes of C-DTMD systems and provides a basis for the optimal design of damper parameters. [ABSTRACT FROM AUTHOR]

The article is focused on the frequently discussed topic of suburban and regional railway system modernization comparing different approaches to consider by local authorities. To make a precedent and to better contribute to practical problems, a middle-sized city of Olomouc (Czechia) is chosen to demonstrate the upgraded regional transport system design with respect to the regional specifics and utilizing of the current railway and tramway infrastructure. With a new, innovative approach to the potential tram-train implementation in the Olomouc Region, the technical and operational features of the current tram-train systems and vehicles are analysed. By implementing the most suitable ones, the regional tram-train system proposal in the conditions of Olomouc may serve as a guide for similar planned systems in the countries with limited legislative and technical experience regarding the tram-train systems (especially in Central Europe), their suitability for the region, their design, and introduction. For the qualitative assessment of the different relevant approaches, a comparative analysis is used. As a result of the analysis, a tram-train system is recommended for the further transport planning stages as it significantly improves both urban and suburban public transport in the Olomouc Region by increasing their average speed and accessibility. For better financial and technical implementation, the project is divided into several stages. Unlike the already presented solutions, it combines shorter travel times of the regional rail and the populated area coverage reached by regional buses and serves as both an attractive and financially sane extension of a regional public transport system.

California sits at the epicenter of self-driving vehicle technology development, with numerous companies testing connected and automated vehicles (CAVs) in the state. CAVs have the potential to improve safety and increase mobility for children, the elderly, and people with disabilities. These vehicles will operate more efficiently, use less space on the roadway, and cause fewer crashes, all of which are expected to relieve traffic congestion. However, CAVs will also likely bring about complex changes to travel demand, urban design, and land use. The degree to which these changes will affect vehicle miles traveled, energy use, and air pollution in California is unknown and could have wideranging implications for the state’s ability to meet its climate goals.Researchers at the University of California, Davis investigated the range of potential impacts that rapid adoption of CAVs in California might have on vehicle miles traveled and emissions. The researchers estimated the vehicle miles traveled and emissions of each scenario using a statewide travel demand model, emissions factors from California agencies, and assumptions derived from the scientific literature and expert input. This policy brief summarizes the findings from that research and provides policy implications.View the NCST Project Webpage

One of the ways to improve energy efficiency in transportation is through efforts aimed at increasing the usage of public transportation by residents. This, in turn, is closely related to residents’ preferences. One of the most frequently cited factors influencing the attractiveness and quality of public transportation is the frequency of connections. This is important not only for urban transport but also regional transport, which has significantly lower passenger flows. This paper aims to present how the frequency of connections affects the attractiveness of regional transport. An original method for determining the attractiveness of public transport, based on the share of adult, senior, and youth passengers with single tickets and monthly passes, is introduced. The results of research on the structure of passengers and flows to/from the district center are presented. Based on the research results, attractiveness factors are calculated for each town. The statistical analysis clearly indicates there is a strong correlation between the attractiveness of regional transport and the frequency of connections (Rs = 0.807, p = 0.001). Moreover, for every connection increase, the number of adult passengers will increase by an average of 1.5. Assuming that these additional passengers switch from individual to public transportation, the resulting reduction in energy consumption due to an increase of one connection is 0.33–0.69 kWh for each kilometer traveled by these passengers.

The California Department of Transportation set a goal of doubling walking and transit use and tripling bicycling in the state between 2010 and 2020. However, the most recent comprehensive travel surveys, the 2012 California Household Travel Survey (CHTS) and the California results from the 2017 National Household Travel Survey (NHTS), suggest that the state is moving in the wrong direction. These surveys seemed to show that a smaller share of trips were made by walking or biking in 2017 than in 2012, while private vehicle mode share increased. It is unclear whether the decline represents real changes stemming from various demographic or other factors or is instead related to methodological differences between the two surveys. Researchers at the University of California, Davis used the publicly available 2012 CHTS and 2017 NHTS California add-on data to examine the impact of methodological differences on the changes in mode shares over this five-year period and conducted a preliminary investigation into the role of demographic and other factors in these changes. This policy brief summarizes the findings from that research and provides policy implications.View the NCST Project Webpage

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This study evaluates changes in travel mode shares in California over the period from 2012, when the California Household Travel Survey (CHTS) was most recently completed, to 2017, the most recent implementation of National Household Travel Survey (NHTS). Initial review of the data suggests decreases in biking and walking over this time period. This study explores the factors contributing to this change, namely are these apparent changes the artifact of methodological differences between the analysis of the two surveys, or do they reflect real changes in the travel behavior of Californians? The piece also explores external factors, or changes over time that may contribute to mode share changes, such as demographic shifts or system-wide shocks such as the Great Recession. There are many differences in the preparation of the data used in the analysis across the two surveys; for example, the variables included in weighting are not the same for the CHTS and the NHTS. These differences are not found to have an impact on the outcomes of interest; however, they do suggest the need for more coordination among the NHTS and the CHTS to better enable comparative studies.View the NCST Project Webpage

This study helps understand how the anticipated emergence of autonomous vehicles will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. The study begins with a literature review on connected and automated vehicle (CAV) technology for light-duty vehicles, the factors likely to affect CAV adoption, expected impacts of CAVs, and approaches to modeling these impacts. The study then uses a set of modifications in the California Statewide Travel Demand Model (CSTDM) to simulate the following scenarios for the deployment of passenger light-duty CAVs in California by 2050: (0) Baseline (no automation); (1) Private CAV; (2) Private CAV + Pricing; (3) Private CAV + Zero emission vehicles (ZEV); (4) Shared CAV; (5) Shared CAV + Pricing; (6) Shared CAV + ZEV. The modified CSTDM is used to forecast travel demand and mode share for each scenario, and this output is used in combination with the emission factors from the EMission FACtor model (EMFAC) and Vision model to calculate energy consumption and criteria pollutant emissions. The modeling results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled and emissions will likely increase, due to the relative convenience of CAVs. The study also reveals limitations in models like the CSTDM that primarily use sociodemographic factors and job/residence location as inputs for the simulation of activity participation and tour patterns, without accounting for some of the disruptive effects of CAVs. The study results also show that total vehicle miles traveled and vehicle hours traveled could be substantially impacted by a modification in future auto travel costs. This means that the eventual implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help curb tailpipe pollutant emissions in future scenarios, though they may not be able to completely offset the increases in travel demand and road congestion that might result from CAV deployment. Such policies should be considered to counteract and mitigate some of the undesirable impacts of CAVs on society and on the environment.

Transportation is undergoing its most significant transformation since the inception of road transport. This transformation is fueled by concerns about climate change and technological advancements. Understanding the current situation in mediumsized cities across Europe is crucial to developing transportation concepts for urban areas within the EU. This study offers insights into the current state of urban mobility in postcommunist countries and depicts the current spatial mobility in a medium-sized city. The aim of this study is to evaluate the influence of selected socioeconomic factors on the mobility of employees and students. This study, conducted in 2019, employed quantitative research methods, including a questionnaire survey. In total, 545 employees and students participated in the survey, representing a significant indicator of mobility in the city of Zlín. Data analysis was performed using nonparametric methods. The results indicate a relationship between respondents' job classification, age group, transportation time, and choice of means of transport. These findings can be applied to countries with similar historical and cultural development backgrounds, such as Poland, Hungary, and Slovakia. [ABSTRACT FROM AUTHOR]

The behavioral features of the population are addressed in transport models by different levels of territorial disaggregation and the creation of demand strata in a territory. The need for input data grows exponentially with the demand for a detailed zonal system of the territory. The basic source is the mobility survey. This article deals with the comparison of the calculation of the probability of choosing a transport mode for trips using the classic multinominal logit model and the best-worst method. We used data from a mobility survey in the Žilina region as a basic sample. The analysis covered 11 districts and their gravity areas. The individual transport relations are evaluated in detail in the analysis. The results confirm the high degree of accuracy of the best-worst method in the calculation of mode choice on a regional scale. Despite the promising results of the agreement in the confrontation with the mobility survey, it is necessary to verify the modeled data with a more detailed area with disaggregation on-demand strata.

The study of commuting mode choice is crucial since driving, with all its associated environmental and economic consequences, is the United States’ most popular mode of transportation due to urban sprawl, priority to road construction and America’s love affair with the automobile. More attention needs to be paid to sustainable modes such as public transit and walking. The built environment is expected to have an impact on commuting mode choice. Built environments with higher density, diversity, intentional design, destination accessibility, and shorter distance to transit (collectively known as the 5 Ds of the built environment) are hypothesized to lead to more sustainable mode choices, including public transit and walking. In this paper, we evaluate the impact of built environment variables on commuting modal split, including the four modes of public transit-bus, public transit-rail, walking, and driving. The study is conducted in Mecklenburg County, North Carolina, at the geographic level of census block groups in year 2015. Given the complexity of relationships in the built environment-travel behavior subject, the random forest method is used to predict aggregated commuting mode choice. Random forest is employed as it is capable of capturing nonlinear relationships and is not constrained by limitations in other widely used methods, such as multinomial logistic regression. After predicting the commuting mode shares, SHAP values (SHapley Additive exPlanations) are used to evaluate the impact of the built environment on commuting mode choices. As an advanced machine learning method, SHAP values adds explainability to the model. This method resolves the known limitation of machine learning methods as being “black boxes” and converts them to “white boxes” by providing interpretability. They provide insights into both the direction and magnitude of the relationships. Thanks to its rigorous ML-based design, our study helps to solidify the state of knowledge with strong evidence that block groups with higher degrees of the 5Ds lead to more choices of public transit and walking modes. We discuss urban policy implications of this study.

In many cities worldwide, various urban problems such as traffic congestion and environmental pollution are becoming increasingly serious due to traffic. As global warming intensifies due to various causes, the Science Based Targets initiative has proposed a “Net-Zero” standard for net-zero greenhouse gas emissions. Many cities are adopting more sustainable and efficient alternative modes to replace existing transportation modes. In this study, we used Uiwang, Gunpo, and Ansan of the 3rd New Town in Korea as demonstration areas and analyzed the modal split process to investigate how much modal split can be generated from existing passenger car users if a tram, bus rapid transit (BRT), and super BRT (S-BRT) are presented as eco-friendly and feasible transportation modes. Subsequently, we analyzed the environmental impact of the tram, BRT, and S-BRT as the number of public transportation users increased. The analysis results show that the modal split to public transportation of the tram, BRT, and S-BRT occurred at 16,657, 20,990, and 24,268 trips/day, respectively, while the resulting air pollution costs were USD 46.01 million/year, 44.89 million/year, and USD 43.92 million/year, respectively. These results indicate that the S-BRT is environmentally friendly mobility and advantageous in terms of cost. The findings of this study demonstrate that the S-BRT is an effective public transportation mode for carbon neutrality.

Motivating people to switch to public transport from using their own car is one of the most important parts on the way to accomplishing the Green Deal 2050 challenge. In the Czech Republic, where the number of passengers was rapidly rising in the pre-pandemic time, individual car transport still offers many more travel benefits than railway lines for most long-distance relations. How to strategically develop the railway infrastructure? Will the planned high-speed railways really be the appropriate solution to this problem in time? Will they satisfy all the different requirements of passengers who are potentially able to switch from car to train?

The mode choice of commuters is an important element in the formulation and design of transportation policy. It gives policymakers a better understanding of the dynamics of intra urban trips. However, most of the studies on the modal split of intra urban trips found in the literature focused on Europe, America, and Southeast Asia with little emphasis on Africa. The lacuna this trend creates motivates this study to present evidence from Nigeria using the centenary city of Enugu as a case study. This study is critical for knowledge production that drives explicit outline strategies for the development of a sustainable urban transportation system in Nigeria. Data on modal split and determinant factors were collected from primary source through the use of a questionnaire. Discriminant analysis was used to classify the modal split of intra urban trips in Enugu into two groups' namely public and private transport. Although public transport modes are the most used for intra urban trips, the mode share indicates that private transport mode (car) is the most used single mode for intra urban trips. More than three-quarters of commuters that use private transport for intra urban trips prefer the mode, whereas a little less than one-fifth of commuters will switch to public transport modes. On the other hand, most of the commuters that use public transport for intra urban trips prefer the modes and very few others will switch to private transport. Car ownership has the strongest influence on the modal split. The study identifies the need for policy interventions that reflects better understating of the dynamics of intra urban trips. [ABSTRACT FROM AUTHOR]

The behavioral features of the population are addressed in transport models by different levels of territorial disaggregation and the creation of demand strata in a territory. The need for input data grows exponentially with the demand for a detailed zonal system of the territory. The basic source is the mobility survey. This article deals with the comparison of the calculation of the probability of choosing a transport mode for trips using the classic multinominal logit model and the best-worst method. We used data from a mobility survey in the Žilina region as a basic sample. The analysis covered 11 districts and their gravity areas. The individual transport relations are evaluated in detail in the analysis. The results confirm the high degree of accuracy of the best-worst method in the calculation of mode choice on a regional scale. Despite the promising results of the agreement in the confrontation with the mobility survey, it is necessary to verify the modeled data with a more detailed area with disaggregation on-demand strata. [ABSTRACT FROM AUTHOR]

Despite the ongoing processes of territorial integration, especially in Europe, there are still borders that fulfil their original function, namely that of a barrier. In some cases, this function has even been strengthened. Such is the case with Poland’s eastern border, which is also the external border of the EU and of the Schengen Area. This article presents the modal split of passenger traffic under conditions of frequent changes in the functions and permeability of borders, against the background of the key drivers behind the volumes of border traffic, i.e. the geopolitical, socio-economic, and infrastructural factors, both in relation to road, rail and border infrastructure. All sections of the border display some marginalisation of railway transport. The Polish eastern border is characterised by a sustained high share of bus transport, which pertains to all sections under analysis. The long waiting times for clearance when travelling in private cars was probably one of the factors behind the creation of the market for collective transport. Private transport is most dominant on the Polish-Russian border, while the largest share of crossings by bus is recorded on the Belarusian border.

The Java-trans highway is almost connected from west to the east of Java Island and now, there is a new section that will be built, those are Kertosono-Kediri section and Kediri-Tulungagung section. The Impact from these construction in the future will shorten the travel time. Tulungagung-Surabaya buses which use the highway only take 3-3,5 hours of travel time with route combinations between national and highway roads. This research aims to find the model of mode choice proportion between bus, non-toll bus, cars, and train due to Tulungagung-Surabaya’s highways in 2023. The travel attributes used in this research are travel time and travel cost between bus, non-toll bus, cars, and train. Then, the model could predict the mode choice proportion in the future, after the construction finished. The method for modelling is modal split which uses power function (α) and deterrence function (β). Mode choice model that is used is a modal split with power function (α) with NMAE values 0,5284 which is close to 0. The interpretation of NMAE value is the smaller the value the better the model, because the model would represent the existing condition in which the model could predict future condition.

The pandemic of the COVID-19 virus disease has resulted in significant changes in everyday life. As a result, two-thirds of the urban population has been forced to alter previously established travel habits. With this in mind, the international scientific community wonders if the newly formed travel habits could be maintained after the pandemic is over. This paper presents the findings of a survey conducted among students in Kragujevac prior to and during the COVID-19 virus pandemic. Furthermore, the citizens' expectations regarding the change in travel habits following the end of the pandemic are presented. For a period of five years, the survey was conducted once a year on students at the Faculty of Engineering in Kragujevac (Serbia). According to the survey results, during the pandemic, the car was considered a more comfortable mode of transportation than before. When compared to before the pandemic, the share of student families with two cars increased by 15%. The number of cyclists has also increased, making the lack of bike paths during the pandemic a more visible issue than previously. The students polled believe that once the pandemic is over, they will walk more and continue to rely on public transportation as before.

Mode choice behavior is a key element in public transport planning, as it has a direct impact on the design of urban transport system infrastructure, and is also the basis for urban public transport planning and management policymaking. The model is used to analyze and predict the choices that individuals or groups of individuals make in selecting the transportation modes that are used for particular types of trips. Typically, the goal is to predict the share or the absolute number of trips made by mode. An important objective in mode choice modeling is to predict the share of trips attracted to public transportation, this share eventually used in travel demand modeling in its modal split step. This specialized report is prepared using the mode choice model developed under the Dhaka Mass Rapid Transit Development (MRT Line-5 Northern Route) study adopting stated preference (SP) method to estimate the mode choice. Stated preference methods use in the transport sector to grab the preferences in a set of transport modes to estimate the utility functions which is usually used to predict the user's choice of nonexistent transport mode in the urban network before implementation. [ABSTRACT FROM AUTHOR]

The paper presents an introduction to the method enabling the estimation of the range of investments necessary for the realisation of the mobility policy understood as the correction of the modal split into the sustainable proportion between car and non-car journeys. The models allow the calculation of the number of travellers required to shift into the public transport mode and the scale of selected investments including the development of the transport network, interchanges, rolling stock, and technical infrastructure. The basis of such calculations is the results of traffic surveys. The worldwide context of the study and similar actions are also presented. The paper consists of five sections. The first section contains a review of current problems connected with the sustainable mobility policy and the role of modal split. The second section focuses on the case study with the presentation of the local mobility policy and selected results of complex traffic surveys. The models used to estimate the investment challenges with exemplary calculations and presentation of similar effects of the intervention are described in the next section (3). Section four contains a discussion on the described methodology. The conclusions in section five end the main part of the paper.

• Improve the resilience of the wood transport sector trough: • balancing the modal split by enhancing multi-echelon and multimodal transport modes, • better working conditions of self-loading log truck drivers, • additional storage capacities at train and truck terminals to increase flexibility, • deepening cooperation along the wood supply chain by exploiting digitalization. Wood supply chains are massively threatened by climate change impacts, leading to more frequent and severe forest calamities. Improving the resilience of wood supply chains requires enhanced knowledge about the structure, critical indicators, and challenges of the transport system. Consequently, a comprehensive empirical study of wood transport was conducted in Austria. Stakeholders of the entire wood supply chain participated in an online survey, focus interviews, case studies, and data collection, enabling both qualitative and quantitative analyses. A critical decline in future trucking capacity driven by an adverse age structure of drivers, a significant lack of new job trainees, and an unbalanced share of unimodal truck transport were detected as critical issues jeopardizing resilience. Stakeholders of the supply chain assessed promising coping strategies, such as increasing the modal share of multimodal and multi-echelon unimodal wood transport, enhancing working conditions of self-loading log truck drivers, exploiting digitalization, providing additional storage, and deepening cooperation. Presented learnings and improvement potentials in cooperation between forest owners, transport operators, and industry are highly relevant for supply chains worldwide to reduce common bottlenecks of truck transport, storage, handling, and take-over capacities. Management and policy implications improving wood transport resilience and increasing transport capacity through investment in railroad and terminal infrastructure, raising legal maximum gross vehicle weights, improving the availability of specific rail wagons, and enhancing wood storage capacities proved to be of utmost significance. Future emphasis on research regarding best practices to cope with salvage wood crises, as well as quantitative benchmarks based on the introduced resilience definition and indicators, is strongly recommended. [ABSTRACT FROM AUTHOR]

Abstract Method This paper endeavors to introduce a new approach to modal split estimation. In the frame of the research, a customized model of the recently created Best-Worst Method (BWM) is applied to evaluate mode choice alternatives by transport experts. The integrated BWM model is tested on a real-world case study in Budapest, the capital of Hungary, where a small number of selected experts estimate the modal split of three different groups clustered based on the distance of their commuting. Results The results clearly demonstrate the popularity of public transport among all groups, while car is estimated to be used primarily by short- and mid-distance commuters. The coherence of the responses is tested along with sensitivity analysis and rank correlation comparison. Moreover, the final results are compared to the official modal split data of the city. Recommendations Based on the findings, it can be concluded that the application of BWM results in competitive accuracy compared to the mainstream methodologies, moreover BWM needs significantly less cost and time effort during the survey procedure.

There is an urgent need to devise policy instruments that will reduce GHG emissions to a sufficient extent in order to achieve the climate targets. Approximately 16% of all greenhouse gas emissions are generated by long-distance travel. Our objective is to estimate the impact of a Tradeable Mobility Credit (TMC) scheme − where the total emissions are fixed by design whereas the price is the outcome of travelers' choices − will have on modal split and trip cancellation for long-distance leisure travel in Europe. To this end, we develop a market equilibrium model which accounts for travel and trading decisions. For our case study consisting of a travel demand of more than 300 million trips performed annually between 3,000 city-pair connections, the credit price that emerges in the market equilibrium state is 272€ per ton of CO2 under an emission reduction target of 30%. Due to the TMC, overall long-distance leisure travel demand is reduced by 20% and the share of air among the remaining trips decreases from 50 to 42% whereas the market share of rail increases from 23 to 26%. Modal shifts and trip cancellation rates vary greatly amongst OD-pair connections, depending on the local value-of-time and the extent of modal competition for the respective connection. Our findings contribute to the on-going debate surrounding instruments for stimulating sustainable (im)mobility, in particular in the context of the long-distance travel market. [ABSTRACT FROM AUTHOR]

This paper addresses a problem in evaluating the impacts of internet-app-based dockless public bicycles and public transport (i-PB&PT) on young people's mode choices considering their choice intentions. A method is proposed, based on the theory of planned behavior and structural equation modeling, to identify changes in the modal split and in travelers' willingness to buy and use private cars after having the intention to use the i-PB&PT mode. Guangzhou City was used as the study case, and an stated preference (SP) surveys was conducted. (1) The density of i-PBs was increased and, accordingly, the reduced cost of i-PB use strengthened travelers' intentions to choose i-PB&PT. For example, when the bicycle search time decreased from 4 to 1 min, the i-PB&PT modal split increased from 38.21% to 41.28%, whereas, if the transfer time between PT and i-PB use decreased from 3.5 to 2 min, the modal split of the combined mode increased from 40.72% to 49.59%. (2) Car ownership decreased from 1,190,465 (for the case without bicycles) to 650,556 as the travelers' intention (endogenous latent variable) to choose the combined mode became stronger. In particular, the values of the three exogenous latent variables (E––emotional attitudes, O––satisfaction, and S––subjective norms) increased (E = 2 to E = 5; OS = 1 to O = 4; S = 2 to S = 4). Therefore, the large-scale implementation and widespread use of the i-PB&PT model should significantly decrease car travel and transport emissions. [ABSTRACT FROM AUTHOR]

Vehicular traffic is one of the major sources of air pollution in European cities. This work aims to understand which characteristics of the urban environment could influence mobility-related air pollution, quantify the health impacts of exposure to traffic-derived PM2.5 and NO2 concentrations, and assess the potential health benefits expected from traffic interventions. The health benefits modeled are intended to provide a set of comparable data to support decision-makers and encourage informed decision-making to design healthier cities. Targeting a large geographical coverage, 12 European cities from 9 countries were comparatively assessed in terms of mean daily traffic volume/area, the number of public transport stops/area, and the percentage of green and outdoor leisure areas, among other urban indicators. This was implemented using an open-source data mining tool, which was seen as a useful engine to identify potential strategies to improve air quality. The comparison of urban indicators in the selected cities evidenced two trends: (a) cities with the most heterogeneous distribution of public transport stops, as an indicator of poor accessibility, are also those with the lowest proportion of km dedicated to cycleways and footways, highlighting the need in these cities for more sustainable mobility management; and (b) the percentage of green and outdoor leisure areas may influence the share of journeys by bicycle, pointing out that promoting the perception of green routes is relevant to enhance the potential of active transport modes. Socioeconomic factors can be key determinants of the urban indicators and would need further consideration. For the health impact assessment (HIA), two baseline scenarios were evaluated and compared. One is based on mean annual traffic contributions to PM2.5 concentrations in each target city (ranging between 1.9 and 13 µg/m3), obtained from the literature, and the second is grounded on mean annual NO2 concentrations at all available traffic and urban background stations within each city (17.2–83.5 µg/m3), obtained from the European Environment Agency database. The intervention scenarios modeled were designed based on traffic mitigation strategies in the literature, and set to ranges of 6–50% in traffic-derived PM2.5 concentrations and of 4–12.5% in NO2 concentrations. These scenarios could result in only a 1.7% (0.6–4%) reduction in premature mortality due to exposure to traffic-derived PM2.5, and 1.0% (0.4–2%) due to exposure to NO2, as the mean for all the cities. This suggests that more ambitious pollution abatement strategies should be targeted. [ABSTRACT FROM AUTHOR]

SUMBooST2 research develops universally applicable data science methodology which extracts key urban mobility parameters and origin/destination matrices from the anonymized big data set gathered from telecom operator. The methodology (toolbox) provides transport planners with a method for fast, efficient, and reliable provision of data on movements within the certain area. Origin/destination matrices with modal split will provide transport planners with valid input data for the planning of urban transport systems. The algorithms which separate relevant mobility data from the overall dataset are the unique part of the toolbox. The algorithms to identify passenger car trips are developed in 2020 project SUMBooST, and they are being upgraded in 2021 to detect trips made by active mobility modes and public transport. For the methodology to be valid, it must be implemented in representative number of cities. Previous SUMBooST project included implementation and validation in the City of Rijeka, and SUMBooST2 continues with two other cities, City of Zagreb, and City of Dubrovnik. The aim of the paper is to present innovative toolbox for the boost of sustainable urban planning based on big data science. [ABSTRACT FROM AUTHOR]

Abstract Purpose A fully electrified transport chain offers considerable potential for CO2 savings. In this paper, we examine the conditions necessary to introduce a fully electrified, large-scale, high-speed rail freight transport system in Europe in addition to high-speed passenger trains, aiming to shift goods transport from road to rail. We compare a novel high-speed rail freight concept with road-based lorry transport for low-density high value goods to estimate the potential for a modal shift from road to rail in 2030. Methods To characterize the impacts of different framework conditions, a simulation tool was designed as a discrete choice model, based on random utility theory, with integrated performance calculation assessing the full multimodal transport chain regarding costs, emissions and time. It was applied to a European reference scenario based on forecast data for freight traffic in 2030. Results We show that high-speed rail freight is about 70% more expensive than the conventional lorry but emits 80% less CO2 emissions for the baseline parameter setting. The expected mode share largely depends on the cargo’s value of time, while the implementation of a CO2-tax of 100 EUR/tCO2eq has an insignificant impact. The costs of handling goods and the infrastructure charges are highly influential variables. Conclusion High-speed rail track access charges are a suitable political instrument to create a level playing field between the transport modes and internalize external costs of freight transport. With the given access charge structure, a reduction of the maximum operating speed to 160 km/h has a positive impact on the expected mode share of rail transport while it still reacts positively to a wide range of the cargo’s time sensitivity (compared to a maximum operating speed of 350 km/h). The flexibility of rail freight’s operating speed is important for an effective implementation. Further research should concentrate on time- and cost-efficient transhipment terminals as they have a significant impact on transport performance.

Modal split is one of the most important parameters used in transport planning. Modal split is often used as a base for strategic and operational decisions since it represents the share of different modes of transport in overall trips. The paper uses data [9] on travel characteristics collected using the same methodology, which makes them acceptable for comparison. The key results related to the changes in the use of different modes of transport during COVID pandemic are presented, as well as the attitudes about the primary problems related to mobility in Serbian cities. Statistical analysis defined the links between problems, system conditions and potential solutions, in order to change passenger behavior and finaly achieve a more favorable modal split.

SUMBooST2 research develops universally applicable data science methodology which extracts key urban mobility parameters and origin/destination matrices from the anonymized big data set gathered from telecom operator. The methodology (toolbox) provides transport planners with a method for fast, efficient, and reliable provision of data on movements within the certain area. Origin/destination matrices with modal split will provide transport planners with valid input data for the planning of urban transport systems. The algorithms which separate relevant mobility data from the overall dataset are the unique part of the toolbox. The algorithms to identify passenger car trips are developed in 2020 project SUMBooST, and they are being upgraded in 2021 to detect trips made by active mobility modes and public transport. For the methodology to be valid, it must be implemented in representative number of cities. Previous SUMBooST project included implementation and validation in the City of Rijeka, and SUMBooST2 continues with two other cities, City of Zagreb, and City of Dubrovnik. The aim of the paper is to present innovative toolbox for the boost of sustainable urban planning based on big data science.