Transport Policy Formulation Research Articles

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.

The impact of a low-carbon transport system policy on total factor carbon emission performance: Evidence from 283 cities in China

Reducing carbon emissions from the transport sector is crucial to achieve “carbon neutrality” and is an important component of a low-carbon policy system. In response to the uncertainty of evaluating current low-carbon transport policies, we provide an in-depth analysis and examination of low-carbon transport system (LCTS) policies implemented in China. Using data from 283 Chinese cities between 2006 and 2020, we provide evidence on the policy effects, potential mechanisms, and heterogeneity of LCTS policies on carbon emission performance. Results showed that LCTS policies significantly improved carbon emission performance in pilot regions. The main ways to achieve this policy's objective were to reduce energy consumption in pilot cities, to promote the development of green technologies, and to implement low-carbon transformation of public transport. Moreover, there was heterogeneity in the policy's effects, with the implementation of a LCTS being more effective in eastern and central regions of China and in larger and more compact cities. By analysing the policy's mechanisms and heterogeneity of effects, this article provides a reference for the formulation and selection of low-carbon transport policies.

WITHDRAWN: Evaluating Collaborative Emission Reduction Efforts in Urban Road Passenger Transportation: insights from the City Scale

Road passenger transportation contributes significantly to carbon dioxide (CO2) and pollutant emissions, posing challenges for urban sustainability, particularly in rapidly urbanizing cities such as Fuzhou, which was recognized as a global sustainable development city for 2023. This study addresses the research gap by integrating road green factors, which refer to the ecological attributes of roads, such as green space, vegetation, and tree coverage that contribute to improving air quality and reducing pollution, into emission reduction strategies for city-scale road passenger transportation. This novel approach has not been extensively explored in the existing literature. The purpose of this study was to identify a rational pathway for emissions reduction by analyzing the economic, demographic, transportation infrastructure, road green space, and policy factor impacts on emissions. Employing a system dynamics (SD) model, this research evaluates the emissions from road passenger transportation in Fuzhou from 2010 to 2030 under various scenarios, including a baseline scenario (BS), controlled private cars travel demand (CPD), promotion of new energy vehicles (PVE), improvement in energy efficiency (IEE), increase in road capacity (IRC), and improvement in road green level (IRG). The principal results indicate that by optimizing the traffic structure and integrating other emission reduction measures, peak CO2 emissions can be achieved by 2024 under the three scenarios or by 2023 under the four scenarios. Furthermore, significant reductions in pollutants of 84.62% and 86.07% are projected, respectively. The study concluded that the economic benefits of CO2 emissions reductions consistently exceeded those of carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NOx) reductions, highlighting the importance of a holistic approach to emissions reduction strategies. Based on these findings, policy recommendations were proposed to guide the formulation of effective road passenger transportation policies aimed at achieving sustainable urban development.

Multiple shared mobility services under competition: Empirical evidence for public acceptance and policy insights to sustainable transport

Traffic congestion and roadside emissions are severe and common problems in metropolitans. As a promising and sustainable solution to mitigating these vehicle externalities, shared mobility reduces the required vehicle fleet size for serving a given level of demand by sharing a vehicle among travelers with similar schedules and itineraries. Public acceptance is the key to the success of shared mobility development. This study investigates the acceptance of drivers and passengers of two typical competing shared mobility modes, car-pooling and taxi ride sharing, taking Hong Kong as a case study. For an empirical evaluation, an on-street stated preference survey was conducted, and 829 respondents, including 257 private car owners and 572 non-private car owners were interviewed about their travel preferences in three given hypothetical scenarios. In total, 2,487 observations were collected for calibrating two proposed logit-based discrete choice models for drivers and passengers. The model results show that the out-of-pocket cost, in-vehicle travel time, and out-of-vehicle time are key factors influencing travelers’ decisions toward car-pooling and taxi ride-sharing. An equilibrium model was proposed and an iteration solution procedure was applied to obtain a convergent solution to balance the demand and supply of drivers and passengers for car-pooling services. Furthermore, sensitivity analyses were carried out to examine the effects of variations in proportions of travel cost and taxi fare shared by passengers for car-pooling and taxi ride-sharing, and to assist in the formulation of relevant transport policies.

Scenario analysis of energy consumption and related emissions in the transportation industry-a case study of Shaanxi Province.

In the context of pursuing carbon neutrality and balancing the use of fossil fuels with renewable energy, the transportation industry faces the challenge of accurately predicting energy demand, related emissions, and assessing the effectiveness of energy technologies and policies. This is crucial for formulating energy management plans and reducing carbon dioxide (CO2) and atmospheric pollutant emissions. Currently, research on energy consumption and emission forecasting primarily relies on energy consumption quantities and emission factors, which lack precision. This study employs the low emissions analysis platform (LEAP) model, utilizing a "bottom-up" modeling approach combined with scenario analysis to predict and analyze the energy demand and related emissions in the transportation industry. Compared to previous studies, the methodological framework proposed in this research offers higher precision and can explore energy-saving and emission-reduction pathways for different modes of transport, providing a valuable energy forecasting tool for transport policy formulation in other regions. The forecast results indicate that under the business-as-usual (BAU) scenario, by 2049, the energy consumption and related emissions in Shaanxi Province's transportation industry are expected to increase by 1.15 to 1.85 times compared to the baseline year. In the comprehensive (CP) scenario, the industry is projected to reach a carbon peak around 2033. The study also finds that energy consumption and emissions predominantly originate from private passenger vehicles, highway freight, and civil aviation passenger, which have the greatest potential for emission reduction under the transport structure optimized (TSO) scenario. Therefore, policymakers should consider regional development characteristics, combine different transportation modes, and specifically analyze the emission reduction potential of the transportation industry in various regions, formulating corresponding reduction policies accordingly.

Identifying Factors that Affect Price Determination for Fuel and Vehicles in Sri Lanka: A System Dynamics Approach

Transportation is crucial for the progress of a nation, and sustainable transportation policies are necessary for long-term development. Among these policies, fuel and vehicle pricing strategies significantly impact the economy, society, and environment. To effectively formulate transportation policies, policymakers must understand the factors influencing policy implementation and explore different approaches to fuel pricing, vehicle pricing, and sustainable transportation. This study aims to develop a comprehensive causal loop diagram (CLD) using the System Dynamics (SD) approach to identify factors and interrelationships within the transportation system. The CLD provides a holistic overview of the complexities and feedback loops inherent to fuel and vehicle pricing policies. The findings contribute to evidence-based decision-making and facilitate the formulation of effective and sustainable transportation policies in the country.

Effects of multilevel governance on the design and implementation of sustainable mobility plans

Objectives: The polycentric nature of multilevel governance structures with multiple actors interacting at different levels can facilitate sustainability transitions. This study aims to explore the impact of multilevel governance on the design and implementation of sustainable urban mobility plans within the two-tiered government structure of Sweden. Methodology: The study is based on qualitative review of the academic literature, secondary sources including review of national policy documents and empirical insights drawn from the case study of Umeå, a medium-sized Swedish city which takes part in several national, local, and transnational initiatives to accelerate sustainability transitions. The empirical data is collected through semi-structured interviews with policy experts, policymakers, transport, and urban planners who are responsible for formulation and implementation of transport policies within national and local institutions. Results: The multilevel governance of transport and sustainable urban mobility policies in Sweden produces several positive effects for design and implementation of policies. These include clear division of responsibilities among public authorities at national, regional, and local government level which helps to overcome the problems such as lack of accountability and responsibility. Conclusions: Multilevel governance of sustainable urban mobility policies in Sweden enables local authorities to have access additional resources and knowledge exchange through networks consisting of various public and private actors.

A comparative study on smart transportation policies of Beijing and Shanghai based on three-dimensional framework

ABSTRACT In recent years, smart transportation has been more and more deeply integrated into urban governance, economic development and people’s life now. Current research on smart transportation lacks an examination from the perspective of public policy. And the analysis and experience summary of smart transportation policies are not enough. Therefore, on the basis of literature review on smart transportation policies, this paper selects smart transportation policies issued by Beijing and Shanghai as the research object, constructs a three-dimensional analytical framework containing policy objective, policy instrument and policy thematic characteristics, and makes a comparative analysis on smart transportation policies of Beijing and Shanghai based on text mining and content analysis. And the models of smart transportation development of Beijing and Shanghai are extracted. Finally, this paper puts forward some suggestions to optimize smart transportation policies so as to provide useful reference for the formulation and implementation of smart transportation policies in the future.

Analysis of accessibility components related to the spatial distribution of food warehouses: empirical evidence from Fortaleza, Brazil

This research delves into the spatial distribution of accessibility components related to the placement of food warehouses in Fortaleza, Brazil. This is accomplished by merging spatial analysis and logistic regression with the Moran index. The outcomes expose how warehouses of differing sizes are dispersed across the region, each finding its place within zones featuring distinct accessibility attributes. Furthermore, the study underscores the significance of factoring in the individual accessibility component for the formulation of effective transportation and land-use policies.

Classification of Particulate Matter (PM2.5) Concentrations Using Feature Selection and Machine Learning Strategies

Abstract This research employed machine learning approaches to classify acceptable or non-acceptable particulate matter (PM2.5) concentrations using a dataset that was obtained from the Nairobi expressway road corridor. The dataset contained air quality data, traffic volume, and meteorological data. The Boruta Algorithm (BA) was utilized in conjunction with the Random Forests (RF) classifier to select the most appropriate features from the dataset. The findings of the BA analysis indicated that humidity was the most influential factor in determining air quality. This was closely followed by the variables of ‘day_of_week’ and the volume of traffic bound for Nairobi. The temperature of the site was determined to have a lower significance. The comparison among different machine learning classifiers for the classification of acceptable and unacceptable PM2.5 concentrations revealed that the Extreme Gradient Boosting (XGBoost) classifier displayed superior performance in terms of Sensitivity (0.774), Specificity (0.943), F1-Score (0.833), and AU-ROC (0.874). The Binary Logistic Regression (BLR) model demonstrated comparatively poorer performance in terms of Sensitivity (0.244), Specificity (0.614), F1-Score (0.455), and AU-ROC (0.508) when compared to other ML models. The prediction of PM2.5 has the potential to provide valuable insights to transport policymakers in their deliberations on urban transport policy formulation.

Injury severity analysis of single-vehicle and two-vehicle crashes with electric scooters: A random parameters approach with heterogeneity in means and variances

In recent years, the electric scooter has become one of the most popular means of transportation on short trips. Due to the lag in the formulation of transportation policies and regulations, coupled with the increasing number of electric scooter crashes, there has been growing concern about the safety of pedestrians and electric scooter riders. For the first time in the extant literature, this study aims to analyze injury severity of electric scooter crashes by unobserved heterogeneity modeling approaches. A random parameters approach with heterogeneity in means and variances is utilized to examine the factors influencing injury severity, using data collected from the STATS19 road safety database. Electric scooter crashes are classified as single-vehicle crashes and two-vehicle crashes, with injury severity categorized into two groups: fatalities or serious injuries, and slight injuries. The model estimation was conducted by considering several variables including roadway, environment, temporality, vehicle, and rider characteristics, as well as second-party vehicle and driver characteristics and manners of collision specific to two-vehicle crashes. The results of the model estimation reveal that certain factors had relatively stable effects with the varying degree of crash injury severity outcomes in both single-vehicle crashes and two-vehicle crashes. These factors include nighttime incidents, weekdays, male riders, and an increase in rider age, all of which are associated with more severe injury outcomes. Moreover, the random parameters logit model with heterogeneity in means and variances is more flexible in accounting for unobserved heterogeneity and exhibits better goodness of fit. This study improves the understanding of electric scooter safety, and the finding can better inform public policy regarding electric scooter use to improve road safety and reduce injury severity of electric scooter crashes.

Interactive effects of the built environment and rail transit on commuting behavior: Evidence from Hefei, China

A deep understanding of the underlying mechanisms of the built environment on commuting behavior along with rail transit is considered of great importance for both TOD land use and formulation of transport policies. The impact of the built environmental factors on commuting behavior has been already explored in the literature. However, the main focus has been laid on the separate effects of each factor and the interactions among these factors have been neglected. Along these lines, taking Hefei, China as the case, this work filled this gap by employing a social ecological model to systematically investigate the interactive effects of the built environment and urban rail transit on commuting behavior. From our analysis, it was demonstrated that land-use intensity was negatively correlated with car commuting, and mixed land use was positively related to metro commuting. Additionally, rail access near the workplace plays a key role in reducing car commuting than residential neighborhoods. This work revealed also some interesting findings on the association between rail transit and commuting behavior, which were significantly affected by land use intensity and mixed land-use. Our work provides valuable insights for the TOD land use to effectively reduce car commuting.

The Effect of Public Traffic Accessibility on the Low-Carbon Awareness of Residents in Guangzhou: The Perspective of Travel Behavior

The demand for transportation among urban residents in China is increasing in tandem with the nation’s population growth, rising consumption levels, and increasing car ownership rates. Breaking the existing high-carbon travel practices and reshaping positive low-carbon awareness represents an inevitable way to change existing transportation structures and reduce urban traffic congestion and carbon emissions. A mediating effect model was employed and we found that community satisfaction is an essential variable in the effect of traffic accessibility and travel behavior on low-carbon awareness. First, the impact of residents’ zero and low-carbon actions on their low-carbon awareness is mediated by community satisfaction. Furthermore, compared to high-income groups, community satisfaction exerts a robust mediating influence on low-income groups. The mediating effect of community satisfaction on the relationship between residential proximity to commercial centers and low-carbon awareness among individuals with low incomes is evident. Based on these findings, this paper explores the heterogeneity and associated measures of low-carbon awareness among residents. The conclusion of this study provides suggestions to promote residents’ low-carbon awareness by improving their travel experience from the perspective of community construction, providing scientific reference and a basis for the formulation of transportation policies for low-carbon city construction.

Strategic perspectives of integrating land use and transport development in Sri Lanka: a review

This article reviews the developments in the transport sector policy in Sri Lanka from the time of getting independence from Britain, highlighting the merits, gaps, prospects and opportunities noticeable in the present-day context. While the transport policy formulation happened in a compartmentalized manner focusing on sub-sectors of road, rail, water, and air, it did not seem to have an overall strategic perspective, especially to integrate the planning of human settlements and the sustainable use of limited land area and other resources of the island. Despite the availability of all-encompassing statutes and well-established institutions, large-scale projects in the road sector, the overall development has been ad-hoc and mostly demand-driven rather than as an integrated demand-managed approach. Such disintegration and dissociation with parallel spatial plans such as the Mahaweli Development Project, Greater Colombo Economic Development Commission projects, etc., has hindered both the development of the transport sector and the overall physical development of human settlements from contributing to rapid growth and sustainable socio-economic development of Sri Lanka. The article suggests a few opportunities and prospects currently available to formulate a cohesive and integrated transportation policy that would enable a strategic contribution to overall national development. The paper discusses the Colombo-Trincomalee Development Corridor, the spatial structure proposed in the National Physical Plan 2050, and the Asian Highway Network proposed by the Economic and Social Commission for Asia and the Pacific (ESCAP).

Probit-Based Discrete-Continuous Choice Model to Explore the Relationship Between Car Ownership and Commuters’ Non-Work Activity Durations in Xiaoshan District of Hangzhou, China

In China, a developing country, the car ownership level is much lower than that in developed countries, but transportation policies have been implemented to discourage car ownership and mitigate traffic congestion. However, car ownership (considered as car availability in this paper, meaning that an individual has access to a household private car) may influence travelers’ well-being. To highlight the interrelation between car ownership and travelers’ well-being, this paper develops a probit-based discrete-continuous model to analyze the relationship between car ownership and the duration of commuters’ three major non-work outdoor activities (Act1: shopping and dining; Act2: leisure and entertainment; and Act3: visiting relatives or friends) in Xiaoshan District, Hangzhou, China. Empirical results indicate strong effects of individual and household socio-demographics, built environment attributes, and work-related characteristics on the car ownership decision and the duration of three non-work activities. The analysis shows positive correlations in unobserved factors between the car ownership decision and the duration of Acts1–3, indicating a mutually promotive relationship. Similarly, negative correlations among the duration of Acts1–3 show that non-work activities’ duration is mutually substitutive. These findings will help to better understand commuters’ car ownership decisions and non-work outdoor activity behavior restricted by fixed work schedules in developing countries, which can, in turn, better evaluate the impact of transportation policies (such as car ownership restriction) on travel demand as well as well-being, and provide decision support for the formulation of transportation policies.

Traffic Flow Forecasting in the COVID-19: A Deep Spatial-temporal Model Based on Discrete Wavelet Transformation

Traffic flow prediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into high-dimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.

Violations of mobility restrictions during COVID-19 in five Indonesian cities: A reflection of transport policy-practice gap

Since the detection of the first COVID-19 case in March 2020, the Indonesian government has implemented various mobility restrictions as a policy response to address the pandemic. To date, violations of mobility restrictions have been discussed in relation to public health risk, but rarely analyzed in terms of understanding the transport policy-practice gap. Using content analysis of news media from March 2020 to May 2021, this article identifies individual actions and institutional factors enabling violations of mobility restrictions. Our findings infer a policy-practice gap regarding operationalization, institutional issues, and lack of consideration of target groups’ behavior. These findings provide insights for transport policy formulation in uncertain times, such as the post-pandemic, especially in the context of rapidly growing Asian cities.

Transport vulnerability: Measuring travel time and expenditure budget in Addis Ababa

Public transport is critical for urban dwellers as it is a key link to access economic opportunities and social services. However, transit services in most developing nations are characterized by longer journey time, high fare, unsafe and uncomfortable travel. Hence, this study seeks to analyze the travel experience of public transit users through the lens of vulnerability. The main objective of the research is to investigate transit users’ expenditure on travel, both in terms of time and money, which are regarded as “budgets” allocated to travel. The analysis is based on a survey distributed to 457 public transit users in Addis Ababa. The study applied proportions, percentages and cross-tabulation of key variables and statistical methods, such as one-way ANOVA to analyze the level of vulnerability of transit users. The result shows that low- and middle-income groups bear a higher level of travel burden, in terms of total travel time and percentage of income spent on transport than high income groups. Transport expenditure accounted above 20% of household income for 88% of low-income groups. However, it is below 15% of household income for high income groups, which reveals less financial burden on them. In terms of travel time, 63.8% of middle income groups experience a total travel time of more than 2 h and 52.3% of low-income groups but only 36.3% of high-income groups devote a total travel time above 2 h. Therefore, transport policy formulation should consider the needs and challenges of various groups of transit users.

Environmental Protection - Sustainable Development - Transport: Chronology of the Approach and Political-Strategic Framework

<p>Since the need for movement/transport has conditioned the development of transport networks, the management of the transport infrastructure largely depends on the guidelines or the policies for the development and management of transport. However, although there were concerns about the impact on the environment in the past five decades, it was only in the early 21st century that the effects of transport on the environment were adequately evaluated through the formulation of transport policies. It is important that a global consensus has been reached on the fact that the impacts of transport infrastructure and transport on the environment are essential. Still, it is far more important for the policies of protecting and improving the environment not to conflict with economic competitiveness so that properly formulated regulation could lead to discoveries and improvements, which would result in a win-win situation for both the public and the manufacturers and the improvement of the credibility of competent institutions and organisations in particular. Through a chronological summary of the transformation, this paper moves from the basic environmental protection toward sustainable development in global and European frameworks, and it emphasises the essential aspects that must be addressed in defining a political and strategic framework for the management of transport infrastructure and transport in underdeveloped and developing countries to meet the requirements for sustainable development.</p>

Road Transport Infrastructure and Economic Growth in Kenya

The empirical debate on the role of road transport infrastructure on domestic economic growth is inconclusive and is characterized by two main opposing views: the road-led economic growth hypothesis and the distorting effect of infrastructure growth hypothesis. The objective of this paper is to examine the effect of road transportation infrastructure on economic growth in Kenya using time series data over the period 1990-2020. Empirically, the study develops a transport-growth model that is an extension of Solow (1956) neoclassical growth function and estimates the model with time series data of Kenya. The study adopted Autoregressive Distributed Lag (ARDL) model and Granger causality approach as the technique for testing the study relationship. The key finding of this study demonstrated that the growth in road length as proxied by road sector expenditure contributed positively to economic growth in both short-run and long run. Applying error correction model, it was observed that there is causality between road infrastructure development and economic growth. This study, therefore, interrogates and accepts the infrastructure–growth hypothesis that increased road transport infrastructure stimulates long-run economy expansion in the Kenyan context. The study suggests that expansion of road transport infrastructure will lead to substantial growth of the Kenya economy though gross capital formation. Therefore, within its stated scope, this finding informs the formulation of relevant and vibrant study transport policy to boost road infrastructure growth and hence sustainable economic growth in Kenya. JEL Classification: H54, E23, C23 Keywords : Transport infrastructure, economic growth, road, public–partnership, error correction model, and road financing, HDM-4. DOI: 10.7176/JAAS/80-01 Publication date: September 30 th 2022