Estimating emissions based on the integration of microscopic traffic simulation and vehicle dynamics model

This study proposes an integrated simulation approach, which consists of a microscopic traffic simulation model, a vehicle dynamics model, and an emission estimation model, in order to estimate emissions based on more reliable vehicle performance measures. The vehicle performance measures such as engine power and engine speed significantly relate to the amount of emissions, and road curvatures and inclinations are the core inputs affecting these vehicle performance measures. Therefore, providing reliable vehicle performance measures reflecting the road geometric attributes is critical for a reliable emission estimation. This study proposes to use the microscopic traffic simulation model for generating vehicle trajectories, which is advantageous in modeling various traffic situations, and the vehicle dynamics model for producing the vehicle performance measures based on the vehicle trajectories. Finally, the outputs from the vehicle dynamics model are fed into the emission estimation model to compute emission measures. This study conducted a case-study using two road sections, one is a hypothesized road section, including various curvatures and inclinations with regular variations, and the other is a Kesselberg road section, which is an actual geometry in Bayern, Germany. The emission measures are estimated in these case-study road sections using both an existing simulation approach and the proposed integrated simulation approach. The difference between these two emission estimation approaches is discussed in terms of the emission measures, including fuel consumption, nitrogen oxides, and particulate matters.     

Network structure and travel patterns: explaining the geographical disparities of road network vulnerability

Inevitably, links in the road network are sometimes disrupted because of adverse weather, technical failures or major accidents. Link closures may have different economic and societal consequences depending on in which regions they occur (regional importance), and users may be affected differently depending on where they travel (regional exposure). In this paper we investigate in what way these geographical disparities depend on the road network structure and travel patterns. We propose aggregate supply-side (link redundancy, network scale, road density, population density) and demand-side (user travel time, traffic load) indicators and combine them in statistical regression models. Using the Swedish road network as a case study, we find that regional importance is largely determined by the network structure and the average traffic load in the region, whereas regional exposure is largely determined by the network structure and the average user travel time. Our findings show that the long-term vulnerability disparities stem from fundamental properties of the transport system and the population densities. Quantitatively, they show how vulnerability depends on different variables, which is of interest for robust network design.     

Modeling AADT on local functionally classified roads using land use,road density,and nearest nonlocal road data

The focus of this research is to model the influence of road, socioeconomic, and land-use characteristics on local road annual average daily traffic (AADT) and assess the model's predictability in non-covered location AADT estimation. Traditional ordinary least square (OLS) regression and geographically weighted regression (GWR) methods were explored to estimate AADT on local roads. Ten spatially distributed counties were considered for county-level analysis and modeling. The results indicate that road density, AADT at the nearest nonlocal road, and land use variables have a significant influence on local road AADT. The GWR model is found to be better at estimating the AADT than the OLS regression model. The developed county-level models were used for estimating AADT at non-covered locations in each county. The methodology, findings, and the AADT estimates at non-covered locations can be used to plan, design, build, and maintain the local roads in addition to meeting reporting requirements. The prediction error is found to be higher at urban areas and in counties with a smaller number of local road traffic count stations. Recommendations are made to account for influencing factors and enhance the local road count-based AADT sampling methodology.     

A multi-criteria analysis framework including environmental and health impacts for evaluating traffic calming measures at the road network level

Recent interests in both vehicle emissions and public health have facilitated the development of more eco-friendly transportation systems. This study developed a multi-criteria evaluation framework to evaluate the effectiveness of traffic calming measures (TCMs) from the various perspectives at the road network level; operational efficiency, traffic safety, environmental and health impacts. The proposed methodology employs four-step sequential simulation experiments, including driving, traffic flow, emissions, and air dispersion simulations. The results obtained from these four simulations are used to evaluate the effectiveness in terms of safety and operational efficiency in addition to environmental and health impacts. A multi-criteria value function based on the weights estimated from the analysis of an analytical hierarchy process (AHP) is applied in the evaluation framework. As an application, chicanes and speed humps widely implemented in Korean school zones were evaluated at the road network level. The proposed simulation-based approach is expected to be effectively used for the decision-making process in selecting better alternatives for TCM.     

Recent experiences and divergent pathways to transport decoupling

This study examines the transport decoupling pathways of the 16 selected countries from 1990 to 2015. Transport carbon emissions (i.e. decarbonisation) and road traffic fatalities (i.e. defatalisation) have been chosen as proxies of environmental and social decoupling respectively. Real GNI in purchasing power parity (based to 2011) is used to reflect the decoupling experience. To frame the transport decoupling pathways, we have applied three decision rules and assigned rankings to the eight decoupling categories identified for richer and poorer countries. Each country has five data points for the ranking (five time periods in the 25-year horizon), meaning that a total of 80 data points have been identified and mapped. Four types of decoupling pathways are derived: Improving, Stable 1, Stable 2 and Unstable. Decoupling policies have then been extracted and analysed to identify the different approaches used in each pathway type. The results show that (i) there is no single pathway to transport decoupling, meaning that a wide range of policies have been adopted by individual countries, (ii) most pathways belong to the ‘Stable’ categories with mild fluctuations in the ranks over time and (iii) defatalisation is more successful than decarbonisation in achieving absolute decoupling. The value of this paper is in its novel methodology that has been used to identify the different pathways for decoupling, its application to 16 key countries, and the use of a database that extends over 5 time periods. It makes a clear contribution to our understanding of the complexity of decoupling, the different pathways adopted, and the difficulties of achieving substantial reductions in transport carbon emissions and traffic fatalities.     

Population change and accessibility by road and rail networks: GIS and statistical approach to Finland 1970-2007

This study analyzes the relation of accessibility by road and railway network to population change between the years 1970-2007 in Finland. Accessibility is evaluated at built-up area unit and municipal levels by potential accessibility analysis and by measuring accessibility to network. Analyses are done in decadal periods by using geographical information systems (GIS) and data about road and railway networks involving digitized speed limits and geometry for each period. Accessibility variables and population change are related by generalized additive models (GAMs). The results indicate that the Finnish population has concentrated to areas with high road-based potential accessibility, especially since the opening in the Finnish economy in the 1990s. The accessibility of railway network was found to have affected the population change in the 1970s, when local level traffic reduced in the entire country, and in 2000-2007, following remarkable investments in long-haul transport.     

Network based definition of functional regions: A graph theory approach for spatial distribution of traffic flows

Functional regions are autonomous (internally coherent and externally self-contained) spatial structures based on vector data, so-called spatial interactions. Typically, travel-to-work, travel-to-school flows and migrations are analysed by various methods of functional regional taxonomy in order to define functional regions. There is still another type of statistically recorded vector data which has, up to now, rarely been used for this purpose: traffic flows. However, these data differ distinctly from the above mentioned flows. In this paper we pursue two objectives: (i) to define functional transport regions based on a graph theoretic analysis of individual traffic flows, and (ii) to add knowledge to the issue of the self-containment of functional transport regions. The specific nature of transport data compared to the above-mentioned spatial interactions requires a specific methodological approach, which is presented in the paper. The existing graph theoretic procedures do not seem suitable for the definition of functional transport regions due to data specifics. Therefore our analysis is based on a rough analogy to the minimum cut method – we identify minimum flows in a graph representing a transport network. The territory of the Czech Republic is used as the example. Two regional systems are defined (based on 2000 and 2016 data) and compared in time. The paper achieves two main findings. First, the proposed methodological approach allows us to define autonomous functional transport regions, and the means to calculate their self-containment is discussed. Second, functional transport regions in the Czech Republic show unexpected stability over time compared to functional regions based on such spatial interactions as commuting flows.     

Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001

Traditionally, the transport literature reflects the view that traffic volumes, road traffic volumes in particular, are coupled with Gross Domestic Product (GDP). Recently published literature also argues that the carbon dioxide (CO₂) emissions from transport, passenger cars in particular, have not shown any decoupling from transport volumes for some years. This article presents a theoretical framework for decoupling, defining the difference between decoupling, coupling and negative decoupling. These are further broken down to weak, strong and expansive/recessive degrees of decoupling, laying emphasis on the absolute increase or decrease of the variables. The result section presents data of the development of the relationships between GDP, traffic volumes and CO₂ emissions from transport in the EU15 countries between 1970 and 2001, including the special case of Finnish road traffic. The aggregate EU15 data show a change from expansive negative decoupling to expansive coupling regarding passenger transport, and from weak decoupling to expansive negative decoupling regarding freight transport. Weak decoupling of transport CO₂ emissions from GDP could also be observed. Weak decoupling of all the three aspects (freight, passenger and CO₂) could be seen in the UK, Sweden and Finland in the 1990s. In Finland, the statistics show weak decoupling of GDP from road traffic volume and strong decoupling of road traffic volume and CO₂ emissions from road traffic between 1990 and 2001. Four hypothetical explanations of the Finnish phenomenon are put forward in this article: policy towards sustainable mobility, green urban lifestyle, increasing income differences, and statistical misinterpretation. Each explanation is backed up with some quantitative evidence in observable trends in Finland during the 1990s.     

Life-cycle CO2 emissions and influential factors for asphalt highway construction and maintenance activities in China

This study tried to clarify the magnitude of CO₂ emissions from highway construction and maintenance in China through life cycle assessment (LCA) method. For this, 227 real highway projects constructed from the year 2000 to 2011 in Zhejiang Province, China are classified into six types by two categories of N road (62 projects without grand bridge, great bridge and tunnel) and Y road (165 projects with the same road structures) and three sub-categories of (i) newly constructed road, (ii) replacing pavement road and (iii) full rehabilitated road. Significant influential factors of LCA results were revealed through multivariate linear regression models, combined with data quality assessment and sensitivity analysis. Numerical interval of assessment results indicate that the construction emissions of N highway project are more centralized to no more than 2900 t/lane-km, while Y project have a normal upper boundary of construction CO₂ emissions, about 5000 t/lane-km. The contribution of maintenance to CO₂ emissions probably could exceed that of newly construction both for Y project and N project. In addition, the pavement replacing and rehabilitation could bring about large amount of CO₂ emissions which even match with the CO₂ emissions from cumulative traffic volume during highway's life cycle. There are common factors for six categories. Cement and steel are the top largest CO₂ emissions contributors and sensitive factors for N road and Y road. The LCA results are not sensitive to the local construction materials but sensitive to the emission factor of diesel used in transportation and on-site construction.     

Why we fail to reduce urban road traffic volumes: Does it matter how planners frame the problem?

If the objective of reducing urban road traffic volumes and GHG emissions from traffic is to be achieved, the way in which land use and transport systems in cities are planned and developed needs to change. Despite apparent agreement that this should be done and how it could be done, cities continue to be planned and developed in ways that cause and allow growth in urban road traffic volumes. In this paper we ask how planners frame the ‘transport problem’, and how their framing of the problem affects urban planning, the resulting plans and developments and the urban road traffic volumes. The discussions are based on findings from a case study, a survey and interviews with planning practitioners.     

Planning for an unpredictable future: Transport in Great Britain in 2030

This paper describes a research study, which explores alternative future scenarios for Great Britain in the year 2030 and the implications these have for travel demand and transport provision. Five alternative future scenarios are represented in the GB national transport model and forecasts are obtained for trip making, traffic levels, congestion and emissions in 2030. For all scenarios it is expected that there will be significant traffic growth. Traffic growth is restricted most in scenarios including distance-based road charging on motorways and trunk roads. However, congestion and carbon dioxide emissions are most effectively limited in scenarios with congestion-based road charging, major improvements to urban public transport and investment in new fuel technologies and in improving engine efficiency.     

Potential for reducing carbon emissions from urban traffic based on the carbon emission satisfaction: Case study in Shanghai

The transport sector has attracted much attention as one of the main sources of carbon emissions. In this study, we constructed an optimal urban traffic structure model based on the concept of carbon emission satisfaction in order to estimate the absolute carbon emission reductions that can be achieved, and to objectively analyze the relative difficulty of achieving the emissions reduction goal. By considering Shanghai as an example, we found that rail transit is the dominant mode of transportation and that the proportion of travel in private cars can be greatly reduced, but buses should be maintained at the current level, whereas the proportion of taxis may be reduced slightly. In the existing traffic environment in Shanghai, after optimizing the urban traffic structure, we found that 47.62% of the carbon emissions reduction target can be achieved. However, given the excessive attention paid to the satisfaction of individuals and the government but the lack of consideration of the ecological environment, the carbon emission satisfaction with respect to urban traffic is low at present in Shanghai. Improving the carbon emission satisfaction by reducing the satisfaction of other targets is difficult, and the potential for reducing carbon emissions is limited for transportation. Therefore, Shanghai can only achieve its carbon emission reduction targets by implementing resource allocation, transportation technology, and urban planning measures in order to improve the existing traffic conditions, thereby achieving the goal of reducing carbon emissions but without affecting the satisfaction of other targets.     

Methods of air traffic management in the airport area including the environmental factor

In the modern approach to air traffic management (ATM), issues related to air quality and climate protection have led to the introduction of a growing number of new restrictions instead of having these issues be the objectives of actions taken. The aim of the study was to investigate whether it is possible to improve air quality while controlling pollution emission by introducing changes in the organization of air traffic control at the airport. For this purpose, mathematical models of emissions were created. These concern the movement of aircraft near the airport and emission of carbon dioxide and the spread of human health related substances emitted from a landing airplane's engine. The first case refers to the possibility of using different arrival procedures. The second case was to adapt the Pasquill formula by treating landing aircraft as a sequence of point-source emissions. As a result of applying the designed models and software tools, it was proven that both a change in the arrival trajectory and a change of the runway can contribute to a reduction of CO₂ emission into the atmosphere. The emission maps created for different aircraft approach profiles show the ability to control the local concentration of harmful emissions. The study clearly shows that the current approach of maximizing throughput is not beneficial to air quality. ATM services should consider the use of other variants of air traffic organization, particularly during periods of reduced traffic.     

Road traffic noise in Montreal and environmental equity: What is the situation for the most vulnerable population groups?

Road traffic noise is one of the most detrimental environmental nuisances for the population. Prolonged exposure to high road noise levels can lead to various problems in people's health and well-being. The objective of this article is to determine whether the groups most vulnerable to road noise, that is, children under 15 years old, people 65 years old and over, and the groups most likely to experience high nuisance levels, visible minorities and low-income individuals, are affected by an environmental inequity related to this nuisance. The method of estimating this nuisance employed in the study is based on a combination of several elements: that is, average traffic flows, road geometries, normal atmospheric conditions, and the characteristics of the urban environment. All of these parameters were considered for the 14 boroughs that make up the central portion of the Island of Montreal. Modelling was used to calculate the maximum daily road noise, based in part on the LimA software predictive model and according to the XPS 31-133 computation method. The results obtained from three different statistical tests and spatial regression analyses show that, on the one hand, the groups chosen on the basis of age are not affected by any environmental inequity related to the phenomenon of road traffic noise. On the other hand, low-income individuals and visible minorities live in city blocks marked by road traffic noise levels that are slightly higher than those experienced by the rest of the population.     

Investigating the associations between road network structure and non-motorist accidents

Road networks channel traffic flow and can impact the volume and proximity of walking and bicycling. Therefore, the structure of road networks—the pattern by which roads are connected—can affect the safety of non-motorized road users. To understand the impact of roads’ structural features on pedestrian and bicyclist safety, this study analyzes the associations between road network structure and non-motorist-involved crashes using data from 321 census tracts in Alameda County, California. Average geodesic distance, network betweenness centrality, and an overall clustering coefficient were calculated to quantify the structure of road networks. Three statistical models were developed using the geographically weighted regression (GWR) technique for the three structural factors, in addition to other zonal factors including traffic behavior, land use, transportation facility, and demographic features. The results indicate that longer average geodesic distance, higher network betweenness centrality, and a larger overall clustering coefficient were related to fewer non-motorist-involved accidents. Thus, results suggest that: (1) if a network is more highly centered on major roads, there will be fewer non-motorist-involved crashes; (2) a network with a greater average number of intersections on the shortest path connecting each pair of roads tends to experience fewer crashes involving pedestrians and bicyclists; and (3) the more clustered road networks are into several sub-core networks, the lower the non-motorist crash count. The three structural measurements can reflect the configuration of a network so that it can be used in other network analyses. More information about the types of road network structures that are conducive to non-motorist traffic safety can help to guide the design of new networks and the retrofitting of existing networks. The estimation results of GWR models explain the spatial heterogeneity of correlations between explanatory factors and non-motorist crashes, which can support regional agencies in establishing local safety policies.     

Modeling different penetration rates of eco-driving in urban areas: Impacts on traffic flow and emissions

Climate change and air quality are two main environmental challenges in metropolitan areas. As road transportation is one of the main contributors, public administrations are facing these problems with a number of complementary policy measures: shift to cleaner modes, new fuels and vehicle technologies, demand management, and the use of information and communication technologies (ICT) applied to transportation. Eco-driving is one of the measures that present large fuel savings at individual level. Although these savings are well documented in the literature, few studies focus on how eco-drivers driving patterns affect the surrounding vehicles and the traffic in general, and more particularly what would be the impact when the number of eco-drivers grows. Using a traffic microsimulation tool, four models in urban context have been built, corresponding to the different types of urban roads. Both the base-case and the parameters setting to simulate eco-driving have been calibrated with real data collected through floating vehicles performing the trips with normal and eco behaviors. In total, 72 scenarios were simulated, varying the type of road, traffic demand, and the percentage of eco-drivers. Then, the CO₂ and NO_x emissions have been estimated through a microscopic emission model. The results show that in scenarios with low or medium demand levels and increasing number of eco-drivers, the effects are positive in terms of emissions. On the other side, with high percentage of eco-drivers and high traffic demand, the emissions rise. Higher headways and smooth acceleration and decelerations increase congestion, producing higher emissions globally.     

Predictive spatial network analysis for high-resolution transport modeling,applied to cyclist flows,mode choice,and targeting investment

Betweenness is a measure long used in spatial network analysis (SpNA) to predict flows of pedestrians and vehicles, and more recently in public health research. We improve on this approach with a methodology for combining multiple betweenness computations using cross-validated ridge regression to create wide-scale, high-resolution transport models. This enables computationally efficient calibration of distance decay, agglomeration effects, and multiple trip purposes. Together with minimization of the Geoffrey E. Havers (GEH) statistic commonly used to evaluate transport models, this bridges a gap between SpNA and mainstream transport modeling practice. The methodology is demonstrated using models of bicycle transport, where the higher resolution of the SpNA models compared to mainstream (four-step) models is of particular use. Additional models are developed incorporating heterogeneous user preferences (cyclist aversion to motor traffic). Based on network shape and flow data alone the best model gives reasonable correlation against cyclist flows on individual links, weighted to optimize GEH (r² = 0.78, GEH = 1.9). As SpNA models use a single step rather than four, and can be based on flow data alone rather than demographics and surveys, the cost of calibration is lower, ensuring suitability for small-scale infrastructure projects as well as large-scale studies.     

The effects of area-wide road speed and curvature on traffic casualties in England

Transport provides a range of benefits to society in terms of mobility, access and economic growth. There are however negative impacts of transport, not least in terms of environmental degradation, damage to property, traffic accidents and loss of life. This paper focuses on road traffic accidents, the reduction of which is an important aim of transport policy world wide. The primary objective of this paper is to develop a series of relationships using spatially disaggregated area-level cross-sectional data between different traffic casualties, road traffic speed and road curvature by controlling for other contributing factors associated with area characteristics. The spatial units of the analysis are the 8019 census wards in England. Ward-level casualty data are disaggregated by severity of the casualty (such as fatalities, serious injuries and slight injuries) and by the severity of the casualty related to various road users.The results suggest that increased average speed within a ward is positively associated with total fatalities and serious injuries; and road curvature is found to be negatively associated with road accidents.     

Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling

Collection of Annual Average Daily Traffic (AADT) is of major importance for a number of applications in road transport urban and environmental studies. However, traffic measurements are undertaken only for a part of the road network with minor roads usually excluded. This paper suggests a methodology to estimate AADT in England and Wales applicable across the full road network, so that traffic for both major and minor roads can be approximated. This is achieved by consolidating clustering and regression modelling and using a comprehensive set of variables related to roadway, socioeconomic and land use characteristics. The methodological output reveals traffic patterns across urban and rural areas as well as produces accurate results for all road classes. Support Vector Regression (SVR) and Random Forest (RF) are found to outperform the traditional Linear Regression, although the findings suggest that data clustering is key for significant reduction in prediction errors.     

Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity

Traffic state in the urban network is a direct reflection of the operational efficiency of the urban transportation system. As the busiest period of the day, traffic states during evening peak hours can effectively measure the capacity and efficiency of the transportation system. The primary objective of this study is to investigate how the potential factors affect traffic states during evening peak hours on weekdays. The geographically weighted regression (GWR) approach was proposed to model the spatial heterogeneity of traffic states and visualize the spatial distributions of parameter estimations. Four types of data including traffic state index (TSI) data, point of interests (POIs) data, road features data, and public transport facilities data were obtained from Shanghai in China to illustrate the procedure. According to the results, the GWR model outperformed the ordinary least square (OLS) model in the explanatory accuracy as well as the goodness of fit. The urban form was revealed to have a significant influence on traffic states and strong local variability for parameter estimations was observed. The number of public and commercial POIs, residential POIs, bus routes, bus stops, the average number of lanes, as well as average traffic volumes can significantly affect the traffic states spatially, and the estimated coefficients of each traffic analysis zone (TAZ) vary across regions. The conclusions of this study may contribute to making the planning and management strategies more efficient for alleviating traffic congestion.