Transport Policy Research Articles

Commuter Cyclist Crash Incidence Rate on Cycling Infrastructure in Lyon and Marseille (France)

The aim of this study is to document the crash incidence rate for cyclists when cycling on five different types of cycling infrastructure in two cities in France. A questionnaire was carried out in 2021 with commuter cyclists in Lyon-Villeurbanne (n=780) and Marseille-Aix-en-Provence (n=404). Respondents provided data on their regular cycling trip and on crashes they have experienced. Using a Geographical Information System, the exposure of cyclists on each type of infrastructure was determined. By relating the number of crashes to the number of kilometres travelled, crash rates (per kilometre travelled) and the 95 percent confidence interval were estimated. This study shows that in the two cities studied, the crash incidence rate is significantly higher on contraflow cycle lanes than on the road. On the contrary, shared-use paths (greenways) are associated with a lower crash incidence rate compared to the road. Results for the other types of cycling infrastructure are unclear. Recommendations are discussed. Further studies are required to improve the design of cycling infrastructure and the broader transport policy to support safe cycling.

Exploring potential car trips for long-distance school escorting using smart card data and a household travel survey

Encouraging students to commute by the metro can effectively reduce household car use caused by long-distance commuting to school. This article focuses on the frequency of metro use by groups of students commuting to school based on the assumption that students who use the metro may occasionally be driven to school by their parents. For the 1st time, we propose a school metro commuter identification process that considers the potential behaviour of escorted students, and we study the potential car trips for long-distance school escorting in Nanjing (China) using Smart Card Data (SCD) and a household travel survey from Nanjing. 3 clusters of students who use the metro for commutes to school are identified by frequency of use for possible escorting behaviour based on the commuting day. As possible factors influencing the 3 frequency groups, usage pattern of the metro, entry time, travel duration and the school–housing relationship are extracted from SCD. Furthermore, a multinomial logistic regression model is used to examine the significant factors that influence the grouping of students. The results show that students who use the metro occasionally for a long commuting distance to school are more likely to be escorted to and from school by their parents, especially to school. The later the entry time is to the metro, the more likely that students are to be escorted to school. Additionally, a long school–housing travel duration/distance significantly contributes to parents’ car trips for commuting. The results of this article are valuable for transport policy to reduce car use for long-distance school trips.

Implementation of Car-Free Days in Support of Sustainable Development to Reduce Vehicle CO2 Emissions in Urban Areas

The transportation sector is one of the main contributors to global greenhouse gas emissions, accounting for 18.52% of total emissions, with this percentage reaching 40% in developed countries. This research aims to assess the effectiveness of Car-Free Day (CFD) implementation in reducing emissions in Cirebon, Indonesia, as a contribution to the 13th Sustainable Development Goal on climate action. The research method used a mixed approach, combining quantitative analysis of fuel consumption data with qualitative interviews to gain a comprehensive understanding of the impact of CFD. Referring to the Ministry of Environment and Forestry's National Greenhouse Gas Inventory guidelines, Book I: The results showed that while CFD reduced emissions by 13-26% in the designated lane due to traffic diversion, it also led to an 8-14% increase in emissions in the alternative lane. Therefore, the overall emission reduction effort was not optimal, as the emission reduction in one lane was masked by the emission increase in the other lane. The implication of this research is that a more holistic approach is needed to effectively reduce urban greenhouse gas emissions. The results of this research can serve as a basis for formulating more effective transportation policies, such as extending the duration and coverage of CFD, restricting vehicles on alternative routes, and improving public transportation systems that are more environmentally friendly.

Non-parametric efficiency analysis of urban transport systems

ABSTRACT To promote territorial mobility and reduce environmental impacts at the same time, transport policy often targets (energy-)efficiency of urban transport systems. Thus far though, related studies are focused on particular transport modes or operators. Given, the multi-modal character of urban mobility, the present study suggests broadening the perspective and accounting for public and private transport. Based on a two-stage non-parametric technique, the efficiency of 49 German municipalities alongside transport systems’ inefficiency determinants is assessed. In line with previous studies, we find highly populated municipalities to operate more efficiently than smaller ones. Second, efficiency is driven by modal split and individual mobility patterns, with a positive correlation between territorial mobility and trips made by bike or public transport. Finally, the results suggest that negative impacts of trip density (e.g. congestion) might counter the positive influence of higher compactness on efficiency, once a certain threshold of urbanization is reached.

Air Transport Organization: Competitiveness, Globalization, and Innovation

Introduction. Effective organization of air transportation is the basis of strengthening the positions of airlines in the international air transport system in the conditions of deepening globalization processes of aviation activity.Problem Statement. It is a priority direction for the development of aviation activities within the framework of the concept of the state's transport policy, which ensures the safety of passengers and the transparency of territorial and industrial relations in the conditions of constant changes in the economic environment of the national economy.Purpose. Research of the current state of aviation activity, ensuring the effective organization of air transportation in the transport system and interrelated branches of the national economy.Materials and Мethods. A dialectical-materialist approach, induction and deduction, abstraction and concretization, formalization and analogy have been employed. Statistical documents of international organizations, private and state institutions, publications of research by domestic and foreign researchers have been used as reference material.Results. Five elements of the organization of air transportation at the global level are highlighted: the largest specific weight of passenger transportation in the structure of aviation activity; the aviation industry is promising, which is gradually expanding the boundaries of its niche in the world transport market; the COVID-19 pandemic has negatively affected the aviation industry, changing the centers of economic gravity; active activity of global aviation organizations and associations, which covers most countries of the world; development of a regulatory and legal framework of a global level and global standardization of aviation activities. The challenges of the aviation industry, which arise at the global level, have been characterized.Conclusions. The key prospects for the development of the global aviation industry based on innovative activities are: the latest types of aviation fuel aimed at environmental safety; targeting new layers of potential customers; growth of jobs in the aviation industry; compliance with the accuracy of the departure/arrival schedule; smart service and flight safety.

External Transport Costs and Implications for Sustainable Transport Policy

External Transport Costs and Implications for Sustainable Transport Policy

Factors Influencing Transportation Mode Preferences for Educational Trips Among Dormitory Resident University Students in Kütahya, Türkiye

This study explores the transportation behaviors of university students residing in dormitories in Kütahya, Türkiye, emphasizing their preferred modes for educational trips. Utilizing a Multinomial Logit model, the research analyzes the influence of socio-demographic factors, trip characteristics, and environmental perceptions on mode choice. The results indicate that public transport and walking are the predominant modes, with significant negative associations being observed between car ownership and the likelihood of choosing these sustainable options. Key findings reveal that, as trip distances increase, students are more likely to use public transport, while higher income levels decrease reliance on both public transport and walking. Male students demonstrate a higher preference for these modes compared to female students. Environmental perceptions, including feelings of safety and satisfaction with infrastructure, play a critical role in shaping transportation choices, highlighting the need for improved lighting, walkability, and public transport quality. These insights have important implications for transportation policy, suggesting that reducing private vehicle reliance and enhancing public transport services can significantly promote sustainable travel behaviors. Overall, the study underscores the importance of comprehensive transportation policies that not only enhance infrastructure and service quality but also consider environmental perceptions and safety to promote sustainable travel behaviors among university students.

A scoping review of the design and characteristics of e-bike financial incentives

ABSTRACT E-bikes are recognised as a sustainable mode of transportation with an unmet potential for widespread adoption. However, despite a decade of global implementation, research gaps persist regarding the design and characteristics of incentive programmes for e-bikes and their effectiveness. This review examines different design elements of implemented financial incentive programmes for e-bike uptake in OECD countries. The findings reveal three main components common to these schemes: (1) target cohort, with the majority of programmes focusing on the local population; (2) eligible e-bike types, with regular e-bikes being the most frequently chosen; and (3) financial incentive structures aimed at maximising uptake among the target cohort, with post-purchase rebates being the most prevalent. Another significant aspect identified was the allocation process, predominantly following a “first come, first served” structure. Very few (n = 4) studies included in this review assessed the effectiveness of financial incentives to purchase an e-bike. The variety of designs in these schemes, coupled with a lack of effectiveness assessments and limited evidence, highlights challenges in determining optimal transport policies. In addition to highlighting the knowledge and research gaps, this review synthesises global insights on design elements of financial incentive schemes to boost e-bike uptake, providing a guide of available components of such schemes for programme administrators in designing executive programmes.

A novel construction and evaluation framework for driving cycle of electric vehicles based on energy consumption and emission analysis

The driving cycle (DC) is essential for establishing vehicle emission standards, transportation policies, and urban planning. However, existing driving cycles demonstrate poor representativeness and excessive randomness due to the insufficient capture of driving characteristics. Therefore, a novel framework for constructing and evaluating driving cycles of electric vehicles (EVs) based on energy consumption and emissions analysis is proposed to enhance the representativeness of the constructed driving cycles. First, based on road information, an improved dual-chain Markov chain method combined with the self-organizing mapping (SOM) neural network is introduced for clustering and constructing driving cycles. Subsequently, a double-layer evaluation model oriented towards energy consumption and emissions is established through a combination of model-driven and data-driven approaches to select a representative driving cycle (RDC). Finally, comparative experiments are conducted to evaluate the feasibility and scientific validity of the proposed method in multiple dimensions. The results indicate that the driving cycle constructed in this study demonstrates excellent representativeness, with an emission error of 2.04% and a comprehensive characterization parameter (CCP) value of 0.097. This study emphasizes the necessity of incorporating reasonable constraints in the driving cycle construction. This improved representativeness provides a reliable foundation for electric vehicle design and transportation policy development.

Transportation Policy and Innovation as Determinants of Bus Service Market Design: A Case Study of the German Market

Transportation Policy and Innovation as Determinants of Bus Service Market Design: A Case Study of the German Market

City mobility patterns during the COVID-19 pandemic: analysis of a global natural experiment.

During the COVID-19 pandemic, changes were seen in city mobility patterns around the world, including in active transportation (walking, cycling, micromobility, and public transit use), creating a unique opportunity for global public health lessons and action. We aimed to analyse a global natural experiment exploring city mobility patterns during the pandemic and how they related to the implementation of COVID-19-related policies. We obtained data from Apple's Mobility Trends Reports on city mobility indexes for 296 cities from Jan 13, 2020 to Feb 4, 2022. Mobility indexes represented the frequency of Apple Maps queries for driving, walking, and public transit journeys relative to a baseline value of 100 for the pre-pandemic period (defined as Jan 13, 2020). City mobility index trajectories were plotted with stratification by country income level, transportation-related city type, population density, and COVID-19 pandemic severity (SARS-CoV-2 infection rate). We also synthesised global pandemic policies and recovery actions that promoted or restricted city mobility and active transportation (walking, cycling and micromobility, and public transit) using the Shifting Streets dataset. Additionally, a natural experiment on a global scale evaluated the effects of new active transportation policies on walking and public transit use in cities around the world. We used multivariable regression with a difference-in-difference (DID) analysis to explore whether the implementation of walking or public transit promotion policies affected mobility indexes, comparing cities with and without implementation of these policies in the pre-intervention period (Jan 27 to April 12, 2020) and post-intervention period (April 13 to June 28, 2020). Based on city mobility index trajectories, we observed an overall decline in mobility indexes for walking, driving, and public transit at the beginning of the pandemic, but these values began to increase in April, 2020. Cities with lower population densities generally had higher driving and walking indexes than cities with higher population density, while cities with higher population densities had higher public transit indexes. Cities with higher pandemic severity generally had higher driving and walking indexes than cities with lower pandemic severity, while cities with lower pandemic severity had higher public transit indexes than other cities. We identified 587 policies in the dataset that had known implementation dates and were relevant to active transportation, which included 305 policies on walking, 321 on cycling and micromobility, and 143 on public transit, across 230 cities within 33 countries (19 high-income, 11 middle-income, and three low-income countries). In the global natural experiment (including 39 cities), implementation of policy interventions promoting walking was significantly associated with a higher absolute value of the walking index (DID coefficient 20·675 [95% CI 8·778-32·572]), whereas no such effect was seen for public transit-promoting policies (0·600 [-13·293 to 14·494]). Our results suggest that the policies implemented to mitigate the COVID-19 pandemic were effective in changing city mobility patterns, especially increasing active transportation. Given the known benefits of active transportation, such policies could be maintained, expanded, and evaluated post pandemic. The discrepancy in the interventions between countries of different incomes highlights that changes to the infrastructure to prioritise safe walking, cycling, and easy access to public transit use could help with the future-proofing of cities in low-income and middle-income countries. None.

Development of Particulate Matter Concentration Estimation Models for Road Sections Based on Micro-Data

With increasing global concerns related to global warming, air pollution, and environmental health, South Korea is actively implementing various particulate matter (PM) reduction policies to improve air quality. Accurate data analysis, including the investigation of weather phenomena, monitoring, and integrated prediction, is essential for effective PM reduction. However, the factors influencing the PM generated from domestic road sections have not yet been systematically analyzed, and currently, no predictive models utilize weather and traffic data. This study analyzed the correlations among factors influencing PM to develop models for estimating fine and coarse PM (PM2.5 and PM10, respectively) concentrations in road sections. Regression analysis models were used to assess the sensitivity of PM2.5 and PM10 concentrations to the traffic volume, whereas machine learning-based models, including linear regression, convolutional neural networks, and random forest models, were constructed and compared. The random forest models outperformed the other models, with coefficients of determination of 0.74 and 0.71 and mean absolute errors of 5.78 and 9.60 for PM2.5 and PM10, respectively. These results indicate that the random forest model provides the most accurate PM concentration estimates for road sections. The practical applications of the developed models were considered to inform effective transportation policies aimed at reducing PM. The developed model has practical applications in the formulation of transportation policies aimed at reducing PM. In particular, the model will play an important role in data-driven policymaking for sustainable urban development and environmental protection. By analyzing the correlation between traffic volume and weather conditions, policymakers can formulate more effective and sustainable strategies for reducing air pollution.

Transport Policy Pathways for Autonomous Road Vehicles to Promote Sustainable Urban Development in the European Union: A Multicriteria Analysis

The European Union’s policy aims for the wide-scale deployment of automated mobility by 2030, i.e., within the next programming period (2028–2034), with the deployment of autonomous road vehicles (AVs) in cities playing a key role. Researchers suggest that AV deployment will have complex impacts on urban development, which are difficult to quantify due to scarce real-life data. The present research aims to evaluate different policy pathways of AV deployment for sustainable urban development in the next EU programming period. A multicriteria analysis is conducted, combining AHP and VIKOR, with the participation of experts across Europe. Initially, the potential impacts on sustainable urban development are weighted as evaluation criteria. Then, different pathways are evaluated against these criteria, i.e., AV deployment as collective and/or private transport in specific areas and periods or in the whole Functional Urban Area (FUA) on a 24 h basis. An interesting finding is that the effect on the city’s spatial development, not thoroughly examined by literature, is highly ranked by experts. Regarding policy pathways, autonomous collective transport with 24 h service of the FUA emerged as the optimum alternative. The proposed methodology provides a tool for planners, researchers, and policy makers and a framework for an open debate with society.

Optimizing Traveler Behavior Between MADINA and JEDDA Using UPPAAL Stratego: A Stochastic Priced Timed Games Approach

This study looks at how travelers move between MADINA and JEDDA, using the UPPAAL Stratego tool to tackle the complexities of urban mobility. As cities grow, effective transportation planning becomes more challenging. Travelers have three options: car, bus, and train. The choices for car and bus travel are impacted by traffic conditions, which can vary between heavy and light, affecting both travel time and cost. We propose a detailed mathematical model that captures all possible scenarios related to these travel options, incorporating the uncertainties of real life. This allows us to simulate different traffic situations. By using UPPAAL Stratego, we evaluate three strategies: the Safe Strategy, which minimizes risk; the Fast Strategy, which aims to reduce travel time; and the Fast and Safe Strategy, which seeks a balance between speed and safety. This paper starts with an introduction to the Stochastic Priced Timed Games approach, highlighting its relevance in modeling dynamic travel environments. We then provide an overview of UPPAAL Stratego, showcasing its abilities in generating, optimizing, and comparing strategies. Next, we outline our mathematical model, explaining the assumptions, parameters, and data sources we used. Our simulation results illustrate how each strategy performs under different conditions, shedding light on traveler preferences and behaviors. The findings underscore the significance of accounting for traffic variability in travel planning and offer important insights for urban transportation policies aimed at improving the traveler experience and optimizing resource use. Additionally, we emphasize the theoretical contributions of our model by demonstrating its applicability to real-world scenarios and its potential to inform future research in urban mobility optimization. Ultimately, this research adds to the growing knowledge of smart transportation systems, demonstrating how formal mathematical modeling can address complex real-world challenges and inform future urban mobility strategies.

Impact of urban facilities spatial inequality on sustainable travel mode.

With the implementation of sustainable development objectives in developing countries, urban planning, land use regulation, and urban mobility policies are expected to help reduce inequalities in access to urban facilities. Urban transport policies are also expected to encourage travel by non-motorised modes and public transport. These are considered to be the sustainable modes of urban transport. In this paper, we investigate how inequality of urban facilities impacts trips made by sustainable modes in the city of Santiago de Chile. We use a Poisson regression model and its geographical extension, the geographically weighted Poisson regression model (GWPR). The results suggest that the inequality of urban facilities impacts trips made by sustainable modes. The variables with the highest relevance are the spatial distribution of mixed land use, the spatial distribution of urban services related to transport infrastructure, primary and secondary education, as well as the spatial distribution of demographic variables related to people's life cycle.

Cluster analysis of travel behaviour threats in chosen European functional urban areas during the economic crisis

Motives: Urban mobility models are constantly changing due to economic, social, infrastructural, and geographical factors. Preferences for transport modes vary by country, region, and city, and transport infrastructure and policy have a significant impact on residents’ choices. Additionally, economic factors such as economic crises, public policy decisions, and events such as armed conflicts have a significant impact on the dynamics of mobility in functional urban areas (FUAs).Aim: The main goal of the research was to create groups of countries (clusters) that have similar priorities regarding travel behaviour threats revealed as a result of the economic crisis resulting from the conflict in Ukraine.Results: The study shows that the main threats during the economic crisis (resulting from the war in Ukraine) are economic factors (e.g., ticket prices, an increase in fuel costs), legal factors (e.g., loss of a driving license or passenger transport license), and infrastructural factors (e.g., poor condition of infrastructure). The analysed countries were grouped based on the hierarchical prioritization of threats, using Ward’s hierarchical clustering method. Interestingly, the distance from the conflict epicentre did not significantly influence the prioritization of threat factors influencing transportation behaviour in FUAs. Instead, factors such as membership in international organizations, energy dependence on Russia, the level of economic growth, and transport infrastructure played a more key role in the priority-setting process.

Optimization of transport sustainability index to conserve resources: A case study of Delhi, India

The objective of the study is to propose a methodology for optimization of Transport Sustainability Index (TSI) of National Capital Territory (NCT) of Delhi since maximum value of TSI i.e. 1.0 will occur when all the sustainability indicators attain their extremum values simultaneously, which is highly improbable. TSI is estimated using 29 most appropriate transport sustainability indicators under four pillars of sustainability, viz. environmental, social, economic, and technological. Optimization of TSI involves following steps, viz. identification of three independent variables, i.e. fuel consumption (X1), registered vehicles (X2) and population (X3) which influence all the indicators, data collection in respect of indicators and influencing variables for 30-year time period (from year 2000 to 2030), evolution of regression equations between indicators and influencing variables, and formulation of TSI as a multi-variate non-linear expression in terms of influencing variables, which is used for optimization. The generalized reduced gradient non-linear programming optimization technique is used to solve for the optimum values of influencing variables (X1opt, X2opt, X3opt) as 0.0988, 0.4709 & 0.0 respectively and optimum value of TSI (TSIopt) as 0.71, which is 25% higher than the TSI of 0.59 for the year 2023–24. The knowledge of optimum value of TSI would help the city transport policy planners to allocate, only that much quantum of infrastructural and financial resources so as to reach optimum rather than extremum values, which leads to potential savings or conservation of resources. An overall notional savings of 58% has been estimated by the study.

International experiences on active travel planning: Impact of the COVID-19 pandemic

The COVID-19 pandemic generated significant impacts on transport policy. Exploring barriers and enablers to active travel through the lens of the practitioners planning for and promoting its uptake can uncover underlying mechanisms that shape travel choices and encourage more effective interventions. This study uses online survey data from practitioners working in the planning and provision of active travel related initiatives (N = 40), to understand international experiences, as influenced by the surge of interest in active travel during the pandemic. We base our investigations and analysis on two policy process theories – the Multiple Streams Framework and Punctuated Equilibrium Theory. Drawing tools and concepts from both, we provide a synthesis of international experiences with planning for active travel, and analyse how professionals perceive existing obstacles and opportunities for active travel provision once the immediate impacts of the pandemic have waned. Our findings reveal a pervasive adherence to the status quo – while certain policies temporarily adapted to accommodate the pandemic’s disruptions, the pandemic failed to trigger substantial actions for our sample. The primary challenge lies in the enduring dominance of car-centric cultures and urban form, suggesting both a lack of community understanding about the issues associated with car usage, and ongoing structural barriers of cities planned around cars. Notwithstanding, the emergence of active travel projects and strategies since the onset of the pandemic, coupled with increasing public awareness of active travel, could be valuable in laying the groundwork for future long-term changes, even if their immediate impact is not yet evident.

Assessing the environmental benefits of passenger cars electrification in metropolises: A case study of Singapore

Land-based transportation is a significant contributor to global greenhouse gas (GHG) emissions, with internal combustion engine vehicles being the predominant mode of transport in this sector. As the world population grows and urbanization accelerates, transport demand in metropolitan areas is projected to double by 2050, underscoring the critical need for sustainable transportation strategies, forecasting, and planning within an urban context. One challenge in this endeavor is the insufficient attention given to the distribution of vehicle grades within the overall car population during GHG impact assessment, despite the recognized importance of car grade by researchers and policymakers. In this study, we incorporate both engine type and vehicle grade distribution, derived from data on over 30,000 passenger cars registered with the Land Transport Authority of Singapore between 2018 and 2022, into our life cycle assessment analysis. This approach ensures that our scenario-based studies on prediction and planning are more comprehensive than those that consider only the most popular models. Furthermore, while Singapore benefits from a relatively clean electricity supply, the current limitations in charging infrastructure negatively affect the GHG impact of electric vehicles. The insights from this research will inform the development of transportation policies for metropolises like Singapore, contributing to the advancement of a more sustainable transportation system.

Bayesian multivariate spatiotemporal statistical modeling of bus and taxi ridership

Statistical modeling of ridership over both space and time provides valuable insights on transportation planning and policies. Existing spatiotemporal studies, however, predominantly focus on analyzing a single type rather than multiple types of ridership, thus cannot leverage the correlation between different types of ridership. This study proposes a Bayesian multivariate spatiotemporal statistical model to jointly analyze multiple ridership over time. Specifically, the model accounts for correlation between multiple ridership based on different assumptions of space-time interactions (i.e., departures from the main spatial and temporal patterns) between different types of ridership as well as if covariates are included in the model. Using hourly bus and taxi ridership in the city of Wuhu, China as an example, the case study indicates that accounting for the correlation between the space-time interactions of each ridership, beyond the correlation between the main spatial patterns of the two ridership, further improves the statistical inferences of ridership modeling. In addition, the proposed approach enables the detection of spatial and spatiotemporal hotspots of each ridership as well as bus-taxi ratio hotspots using posterior probabilities. It also supports visual inspections regarding how the inclusion of covariates explains these hotspots. The proposed approach not only advances multivariate spatiotemporal statistical modeling of ridership, but can also provide useful insights on space- and time-specific transport policies at a granular resolution.