Estimating annual average daily traffic and transport emissions for a national road network: A bottom-up methodology for both nationally-aggregated and spatially-disaggregated results

The regular and robust collection of traffic data for the entire road network in a given country will usually require high-cost investment in traffic surveys and automated traffic counters. This paper provides an alternative and low-cost approach for estimating annual average daily traffic values (AADTs) and the associated transport emissions for all road segments in a country. This is achieved by parsing and processing commonly available information from existing geographical data, census data, traffic data and vehicle fleet data. Ceteris paribus, we find that our annual average daily traffic estimation based on a neural network performs better than traditional regression models, and that the outcomes of our aggregated bottom-up road segment emission estimations are close to the outcomes from top-down models based on total energy consumption in transport. The developed approach can serve as a means of reliably estimating and verifying national road transport emissions, as well as offering a robust means of spatially analysing road transport activity and emissions, so as to support spatial emission inventory compilations, compliance with international environmental agreements, transport simulation modelling and transport planning.     

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.     

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.     

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.     

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.     

Land-use and socio-economics as determinants of traffic emissions and individual exposure to air pollution

This paper presents a modeling framework developed for the City of Montreal, Canada, and is intended to quantify two indicators that can explain the spatial distribution of traffic-related air pollution at a metropolitan level. The indicators are estimated at the level of the traffic analysis zone (TAZ) and include: (1) the average level of emissions generated per individual and (2) the level of emissions occurring in a zone as a proxy for air pollution exposure. A regional traffic assignment model is extended with capabilities for emission modeling at an individual trip level while taking into account vehicle (type and age) and trip attributes (road type, speed, and volume). We observe that individuals who generate higher emissions from travel tend to reside in areas with lower exposure to traffic emissions while individuals associated with low levels of travel emissions (e.g. travel smaller distances, conduct less trips, and use alternative modes) reside in areas with high levels of traffic pollution. A regression analysis of the two indicators against a set of land-use and socio-economic variables shows that generated emissions per individual are positively associated with car ownership and larger vehicles, while being negatively associated with ownership of newer vehicles, and location in dense and walkable neighborhoods with high levels of commercial land-use. Meanwhile, exposure to emissions is positively associated with dense and walkable neighborhoods and negatively associated with car ownership and larger vehicles. These findings indicate major inequities in the generation of and exposure to traffic-related air pollution.     

A method for the estimation of greenhouse gas emissions based on road geometric design and its application to South Korea

South Korea has the tenth highest greenhouse gas (GHG) emissions worldwide, of which 16% originates from the road sector. Existing estimation methods of road GHG emissions have various limitations, such as low accuracy or the ability to only estimate GHG emissions within a limited area. Therefore, this study aimed to develop a methodology to estimate GHG emissions while considering various geometric designs of roads, including both vertical and horizontal alignment. The developed method is more objective and reliable than existing methodologies that consider only vertical alignment. First, Lamm's theory on travel speed profiles was applied to predict GHG emissions. Then, this study attempted to overcome the limited spatial estimation capacity of existing methods by considering upstream and downstream geometric design parameters simultaneously. Second, this study used the GHG operation mode extracted from the MOtor Vehicle Emission Simulator (MOVES), a modeling system that estimates emissions for mobile sources at the national, county, and project levels for criteria air pollutants, GHGs, and air toxicity. The operation mode includes vehicle type, fuel, and other factors, and is designed to estimate GHG emissions at 1-s intervals. Based on the results of the analysis, the effectiveness of the new method was compared to existing methods using an economic analysis (e.g., cost–benefits from the reduced emissions). This study presents a method for performing sensitive estimations of GHG emissions according to the geometric design of roads, which can be used to collect more accurate data on GHG emissions.     

Airport traffic complexity and environment efficiency metrics for evaluation of ATM measures

For a given (current or planned) traffic demand, different air traffic management measures could result in different airport traffic complexity and efficiency. This paper presents the research on the relationship between airport traffic complexity and time and environmental efficiency for different air traffic control (ATC) tactics applied to the given or planned airport layout. Emphasis is placed on the evaluation of airport traffic complexity, aircraft fuel consumption, gas emissions and time efficiency for different ATC tactics and/or airport airfield layouts. For busy airports during peak hours, arrival queuing delays, taxi-in, taxi-out times and departure queuing delays increase, which induces additional unnecessary fuel consumption, gas emission and time inefficiency. In order to find a tool which could indicate potential delay generators, a measure of airport traffic complexity – called Dynamic Complexity is proposed. Experiments were performed for airports with different airfield layouts, for different traffic demands and ATC applied tactics using SIMMOD simulation model. Traffic situations were analyzed and delays were measured. The values of airport traffic complexity, fuel consumption and gas emissions were also determined. A comparative analysis of the results show: first, the proposed airport traffic complexity metric quite satisfactorily reflects the influence of traffic characteristics upon the environmental state of the system, and second, different ATM strategic and tactical measures (airport airfield infrastructure development and applied ATC tactics) could significantly reduce traffic complexity and increase time and environmental efficiency at the airport.     

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.     

Potential performance of urban land use and transport strategies in reducing greenhouse gas emissions: Khon Kaen case study,Thailand

ABSTRACT

Transport activities are a key contributor to greenhouse gas (GHG) emissions, global warming, and climate change. In Thailand, private cars are the second largest generator (after trucks) of GHG emissions from the transport sector. This article presents an analysis and evaluation of the implementation of land use and transport measures for reducing GHG emissions in the road network of the Khon Kaen University (KKU) area in Khon Kaen, Thailand. This research applied a bottom-up method to estimate the baseline GHG emissions for several scenarios by adopting the Clean Development Mechanism 2 (CDM2) and Pollution Control Department (PCD) methods over a 20-year planning horizon. The cleaner technology strategy clearly showed the greatest performance in reducing the GHG emission, followed by land use planning and restriction of private vehicle usage. The public transit improvement strategy illustrated the least GHG emissions reduction. Integrated scenarios clearly illustrated larger potential benefits, more effective than the individual scenarios. For both individual and integrated scenarios, the potential performances of the GHG emissions reduction estimated by the PCD method were greater than those assessed by the CDM2 method.     

PM,NOx and CO2 emission reductions from speed management policies in Europe

Speed reduction measures rank among the most common schemes to improve traffic safety. Recently many urban streets or entire districts were converted into 30 kph zones and in many European countries the maximum permissible speed of trucks on motorways is under discussion. However, besides contributing to traffic safety, reducing the maximum speed is also seen as beneficial to the environment due to the associated reduced fuel consumption and lower emissions. These claims however are often unsubstantiated.To gain greater insight into the impact of speed management policies on emissions, this paper examines the impact on different traffic types (urban versus highway traffic) with different modelling approaches (microscopic versus macroscopic). Emissions were calculated for specific types of vehicles with the microscopic VeTESS-tool using real-world driving cycles and compared with the results obtained using generalized Copert-like macroscopic methodologies. We analyzed the relative change in pollutants emitted before and after the implementation of a speed reduction measure for passenger cars on local roads (50–30 kph) and trucks on motorways (90–80 kph). Results indicate that emissions of most classic pollutants for the research undertaken do not rise or fall dramatically. For the passenger cars both methods indicate only minor changes to the emissions of NO_x and CO₂. For PM, the macroscopic approach predicts a moderate increase in emissions whereas microscopic results indicate a significant decrease. The effects of specific speed reduction schemes on PM emissions from trucks are ambiguous but lower maximums speed for trucks consistently result in lower emissions of CO₂ and lower fuel consumption. These results illustrate the scientific uncertainties that policy makers face when considering the implementation of speed management policies.     

Modeling green vehicle adoption: An integrated approach for policy evaluation

This article employs an integrated discrete-continuous car ownership model to jointly forecast households’ future preferences on vehicle type, quantity and use, and to estimate greenhouse gas (GHG) emissions. The model system is estimated on a dataset collected from a web-based stated preference survey conducted in Maryland in 2014. The data contain vehicle purchase decisions and sociodemographic information of 456 households who were requested to state their future preferences over a 9-year period (2014–2022). In each time period, a respondent is faced to four alternatives that include the current vehicle, a new gasoline vehicle, a new hybrid electric vehicle, and a new battery electric vehicle. Intertemporal choices between conventional and “green” vehicles such as hybrid and electric cars capture dynamics in vehicle purchase decisions. Short run and medium-long run situations were predicted and compared based on the first 4-year data and the entire 9-year data of the dynamic panel. Vehicle GHG emissions were calculated correspondingly. We find the introduction of “green” vehicles makes a positive impact on car ownership and use, especially in a medium-long run. Two “green” taxation policies, gasoline tax and ownership tax, were proposed and their impact on vehicle use and emission reductions was evaluated. Results indicate that: (a) gasoline tax is a more effective way to reduce vehicle miles traveled and GHG emissions and (b) gasoline tax makes a higher impact on car use and emission reductions in the medium-long run, while ownership tax makes a higher impact in the short run.     

Factors contributing to injury severity in work zone related crashes in New Zealand

Roadworks take place frequently on existing roads in New Zealand. The adverse effects of poor road conditions and reduced road width due to the presence of a work zone on the safety of road users and workers at the work zone have been a matter of concern. Several studies have been conducted to examine the risk factors contributing to the occurrence of road crashes in work zones in different countries. Slow-moving and stopped vehicles near work zones have been found to be the primary cause of crashes and casualties in the work zones. Excessive speed of passing traffic has also been recognized as a crucial factor contributing to work zone related crashes in New Zealand. This study examined the effect of possible risk factors contributing to severe injury and fatality in work zone related crashes in New Zealand. A multinomial logistic regression model was established to determine the association between crash severity and factors such as road environment, vehicle attributes, driver behavior, and crash circumstances, based on the information available on 453 road crashes during the period from 2008 to 2013. The results indicated that the time period, vehicle involvement, and presence of vulnerable road users were the factors that determined the crash severity in work zones. This implies that improvements are required in traffic control and management measures in work zones to enhance road safety in the long run.     

The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion

The green vehicle routing and scheduling problem (GVRSP) aims to minimize green-house gas emissions in logistics systems through better planning of deliveries/pickups made by a fleet of vehicles. We define a new mixed integer liner programming (MIP) model which considers heterogeneous vehicles, time-varying traffic congestion, customer/vehicle time window constraints, the impact of vehicle loads on emissions, and vehicle capacity/range constraints in the GVRSP. The proposed model allows vehicles to stop on arcs, which is shown to reduce emissions up to additional 8% on simulated data. A hybrid algorithm of MIP and iterated neighborhood search is proposed to solve the problem.     

Performance measurement of a transportation network with a downtown space reservation system: A network-DEA approach

We propose an evaluation approach for a novel travel demand management strategy known as the downtown space reservation system (DSRS). This approach takes into account three perspectives, i.e., transportation service provider’s, the user’s, and the community’s and is based on network-Data Envelopment Analysis (DEA) where the perspectives are inter-related through intermediate inputs/outputs. Two types of network-DEA models (radial and slacks-based models) are considered. An example is provided using data propagated from a microscopic traffic simulation model of the DSRS. The results show that individual node performance can drive network DEA performance and that this information can inform future designs of the DSRS.     

Emissions,performance, and design of UK passenger vehicles

Consumer, legal, and technological factors influence the design, performance, and emissions of light-duty vehicles (LDVs). This work examines how design choices made by manufacturers for the UK market result in emissions and performance of vehicles throughout the past decade (2001–2011). LDV fuel consumption, CO₂ emissions, and performance are compared across different combinations of air and fuel delivery system using vehicle performance metrics of power density and time to accelerate from rest to 100 km/h (62 mph, t_z-62). Increased adoption of direct injection and turbocharging technologies helped reduce spark ignition (SI, gasoline vehicles) and compression ignition (CI, diesel vehicles) fuel consumption by 22% and 19%, respectively, over the decade. These improvements were largely achieved by increasing compression ratios in SI vehicles (3.6%), turbocharging CI vehicles, and engine downsizing by 5.7–6.5% across all technologies. Simultaneously, vehicle performance improved, through increased engine power density resulting in greater acceleration. Across the decade, t_z-62 fell 9.4% and engine power density increased 17% for SI vehicles. For CI vehicles, t_z-62 fell 18% while engine power density rose 28%. Greater fuel consumption reductions could have been achieved if vehicle acceleration was maintained at 2001 levels, applying drive train improvements to improved fuel economy and reduced CO₂ emissions. Fuel consumption and CO₂ emissions declined at faster rates once the European emissions standards were introduced with SI CO₂ emissions improving by 3.4 g/km/year for 2001–2007 to 7.8 g/km/year thereafter. Similarly, CI LDVs declined by 2.0 g/km/year for 2001–2007 and 6.1 g/km/year after.     

Detecting urban road network accessibility problems using taxi GPS data

Urban population growth and economic development have led to the creation of new communities, jobs and services at places where the existing road network might not cover or efficiently handle traffic. This generates isolated pockets of areas which are difficult to reach through the transport system. To address this accessibility problem, we have developed a novel approach to systematically examine the current urban land use and road network conditions as well as to identify poorly connected regions, using GPS data collected from taxis. This method is composed of four major steps. First, city-wide passenger travel demand patterns and travel times are modeled based on GPS trajectories. Upon this model, high density residential regions are then identified, and measures to assess accessibility of each of these places are developed. Next, the regions with the lowest level of accessibility among all the residential areas are detected, and finally the detected regions are further examined and specific transport situations are analyzed.By applying the proposed method to the Chinese city of Harbin, we have identified 20 regions that have the lowest level of accessibility by car among all the identified residential areas. A serious reachability problem to petrol stations has also been discovered, in which drivers from 92.6% of the residential areas have to travel longer than 30 min to refill their cars. Furthermore, the comparison against a baseline model reveals the capacity of the derived measures in accounting for the actual travel routes under divergent traffic conditions. The experimental results demonstrate the potential and effectiveness of the proposed method in detecting car-based accessibility problems, contributing towards the development of urban road networks into a system that has better reachability and more reduced inequity.     

Congestion and multimodal transport: a survey of cargo transport operators in the Netherlands

Longer and more frequent traffic jams in the Netherlands are increasing the costs of transporting cargo. Tightly scheduled production systems rely on punctual materials movements, but are congestion-induced delays sufficient to stimulate freight mode switching from road to combined road–rail and road–water movements? A survey of Dutch transport companies revealed an estimated 10% of vehicle operating time spent in congested conditions. The perceived impact on transport operations, consumers and service characteristics are reported. Based on the survey findings, a vehicle cost simulation attributed 7% of transport costs to congestion, increasing the attractiveness of multimodal transport and other solutions.