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.     

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.     

A closer look at urban development under the emergence of autonomous vehicles: Traffic,land use and air quality impacts

Autonomous vehicles (AVs) hold great promise for increasing the capacity of existing roadways and intersections, providing more mobility to a wider range of people, and are likely to reduce vehicle crashes. However, AVs are also likely to increase travel demand which could diminish the potential for AVs to reduce congestion and cause emissions of greenhouse gases (GHG) and other air pollutants to increase. Therefore, understanding how AVs will affect travel demand is critical to understanding their potential benefits and impacts. We evaluate how adoption of AVs affects travel demand, congestion and vehicle emissions over several decades using an integrated travel demand, land-use and air quality modeling framework for the Albuquerque, New Mexico metropolitan area. We find that AVs are likely to increase demand and GHG emissions as development patterns shift to the region's periphery and trips become longer. Congestion declines along most roadways as expanded capacity from more efficient AV operation outpaces increasing demand. Most of the population can also expect a reduction in exposure to toxic vehicle emissions. Some locations will experience an increase in air pollution exposure and traffic congestion from changes in land-use and traffic patterns caused by the adoption of AVs.     

Green accessibility: Estimating the environmental costs of network-time prisms for sustainable transportation planning

Accessibility, or the ease to participate in activities and obtain resources in a given environment, is crucial for evaluating transportation systems. Greater accessibility is often achieved by increasing individuals' potential mobility. However, potential mobility, if realized by motorized modes, can also generate negative environmental impacts such as fossil fuel consumption and greenhouse gas (GHG) emissions. While the negative environmental impacts of greater mobility are acknowledged, there has been a lack of research to validate those impacts using empirical data, especially considering variations in individuals' mobility levels. This paper presents a method for estimating the expected environmental costs of accessibility represented by a network-time prism (NTP). A NTP delimits all accessible locations within a network and the available time for an individual to present at each location given a scheduled trip origin and destination, a time budget and the maximum achievable speeds along network edges. Estimating the expected environmental costs of a NTP involves three steps: (1) semi-Markov techniques to simulate the probabilities to move along network edges at given times; (2) the speed profiles for reachable edges, and (3) a cost function that translates speeds into environmental impacts. We focus on air quality and employ the motor vehicle emission simulator MOVESLite to estimate the CO₂ emissions at both the edge and prism levels. We calibrate and validate the methods for experimental NTPs defined within the Phoenix, AZ, USA road and highway network using vehicles instrumented with GPS-enabled onboard diagnostic devices (OBD). We demonstrate the effectiveness of our method through two scenarios and investigate the impact of changes in mobility levels on the expected CO₂ emissions associated with the experimental NTPs.     

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.     

Greenhouse gas emissions from flying can offset the gain from reduced driving in dense urban areas

Numerous studies have illustrated how denser urban forms lead to smaller greenhouse gas (GHG) emissions from passenger transport. Many of these studies have excluded aviation since the association between urban structure and air travel is not as intuitive as it is the case of ground travel. However, several recent studies have concluded that air travel is a significant contributor to the GHGs from passenger transport. Furthermore, even air travel habits depend heavily on lifestyles and socio-economic factors that are related to the urban form. Here we analyse the interactions between urban structure and different transportation modes and their GHG impacts in Finland. The study utilises the data from the Finnish Transportation Agency’s passenger traffic survey from May 2010 to May 2011, which includes over 12 000 people and over 35 000 trips. The survey is based on one-day travel diaries and also includes additional data on long-distance trips from a longer period. Methodologically, the study takes a traveller’s perspective to assess the GHG emissions from passenger transport. We found that (1) air travel breaks the pattern where GHG emissions decrease with increasing density of urban structures, and (2) in the metropolitan region there is a clear trade-off between car-ownership and air travel in the middle income class. The main policy implication of our study is that air travel must be included in GHG assessments and mitigation strategies targeting travel behaviour. In dense urban regions, the emissions of air travel have the potential to offset the gain from reduced private driving.     

Analysis of greenhouse gas emissions of freight transport sector in China

Due to the increasing commercial activities in China, the rapid growth of energy consumption and greenhouse gas (GHG) emissions in the freight transport sector has alarmed the Chinese central government. However, there is a lack of standard measure for evaluating GHG emissions generated from freight transport operations. To improve this situation, Chinese policy makers need to evaluate GHG emissions for energy saving and pollution reduction. This background leads us to examine the GHG emission trajectories and features of Chinese freight transport patterns in the last decade, i.e. between 2000 and 2011. In this study, we examine different regions’ freight turnover and energy consumption by various transport modes (i.e. railway, highway, waterway, aircraft, and oil pipeline) in China. Our results show that the total amount of GHG emissions caused by the Chinese freight transport sector reached 978 million tons in 2011, indicating an average annual growth of 74 million tons CO₂e for the last decade. Shandong, Anhui, and Henan are the main provinces producing GHG emissions, representing 11.7%, 10.3%, and 10% of total emissions generated from the freight transport sector in China, respectively. This study also compares the regional GHG emissions from different freight transport modes including railway, highway, waterway, air transport, and oil pipeline. Based on the findings, policy implications are provided on how to mitigate freight transport emissions among different Chinese regions.     

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.     

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.     

Greenhouse gas impacts of different modality style classes using latent class travel behavior model

This study analyzed the interconnections between urban structure and socioeconomic, demographic and lifestyle variables and direct ground transport greenhouse gas (GHG) emissions in the Helsinki metropolitan region in Finland by using a latent class choice model (LCCM). The aim of the study was to identify and improve our understanding of the latent modality styles which guide people's everyday travel choices, and the resulting GHG implications. The GHG implications for different modality style groups are particularly interesting, since such analysis has not been included in previous studies. This study used the transport survey of the Helsinki Region Transport (HSL) from 2012, including over 17,000 trips, and consisted of two parts: first, the population was divided into classes based on latent class analysis, and secondly, the mode choices and GHG emissions were modelled for each class. Seven modality style groups were defined with strongly varying GHG impacts and travel profiles. According to the class specific choice modelling the probabilities of selecting different modes also varied significantly. The study offers new information for designing effective mitigation policies for different types of modality style groups. For example, car-oriented groups would benefit from more fuel-efficient vehicles and vehicles with alternative power sources, whereas the multimodal traveler could be more receptive to policies promoting cycling and public transport. Overall, the study depicts the potential of the latent class method to study the emissions caused by heterogeneous populations and to search for efficient GHG mitigation possibilities.     

TRIBUTE: Trip-based urban transportation emissions model for municipalities

This research aims at developing modeling and scenario-comparison tools to explore the impacts of various transportation and land use planning policies on changing travel behavior and eventually greenhouse gas (GHG) emissions. A Trip-Based Urban Transportation Emissions (TRIBUTE) model is developed. Data required for TRIBUTE comes from household travel surveys and emissions inventories, which is a major advantage in cases where a detailed transportation network model is unavailable. TRIBUTE is composed of two main parts: a mode choice model and an emissions forecasting model. The mode choice model is responsible for estimating modal shares of alternative modes of travel in response to changes in personal, modal, and land use attributes. The emissions forecasting model translates the modal shares into vehicle kilometers traveled, and subsequently GHG emissions. TRIBUTE is a macroscopic model intended to assist municipalities evaluate alternative transportation and land use policy scenarios and eventually select the one(s) that help them meet their future GHG emission targets. This paper reports on the conceptual framework of the developed model and presents a case study.     

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.     

Assessing greenhouse gas emissions from electric vehicle operation in Australia using temporal vehicle charging and electricity emission characteristics

ABSTRACT

Significant interest exists in the potential for electric vehicles (EVs) to be a source of greenhouse gas (GHG) abatement. In order to establish the extent to which EVs will deliver abatement, however, a realistic understanding of the electricity and transport sector GHG emissions impacts arising from different approaches to integrating EVs into the power system is required. A key issue in this regard is the extent to which GHG emissions are a function of where and when EV charging will be enabled (or disabled) by the provision of recharging infrastructure and implementation of charging management strategies by the electricity industry. This article presents an investigation of the GHG emissions arising from electricity and gasoline consumption by plug-in hybrid EVs under a range of standard EV-power system integration scenarios. An assessment framework is presented, and GHG emissions from EV use are assessed for the New South Wales (NSW) and South Australian (SA) pools of the Australian National Electricity Market (NEM) using retrospective electricity system generation data for 2011. Results highlight that there is a range of possible outcomes depending on the integration scenario and emissions accounting approach used. This range illustrates value of a temporally explicit assessment approach in capturing the temporal alignment of electricity sector emission intensity and EV charging. Results also show the importance of a clean electricity generation mix in order for EVs to provide a GHG abatement benefit beyond what would be achieved by a hybrid (but non-plug-in) vehicle. The extent to which overnight charging in NSW is observed to produce higher emissions relative to unmanaged charging also illustrates a possible trade-off between GHG emissions and benefits for electricity industry from EV charging at times of low demand.     

The role of turboprops in China’s growing aviation system

The Chinese aviation system is in a period of rapid growth, with significant growth in second tier and emerging cities. Lower density cities could be well served by regional aircraft, either regional jets or turboprops, which offer different qualities and a different future for Chinese aviation. Turboprops offer a high level of fuel efficiency compared with regional jets which may improve the cost economics for carriers and reduce the air quality and climate impacts of a growing aviation system in a region where air quality and greenhouse gas emissions are a serious concern. However, regional jets are known for their superior quality of service and faster travel speeds. We begin with a spatial analysis of existing Chinese short-haul aviation networks and find that turboprops are deployed in limited number and are dispersed throughout the country. Their limited use, however, is not because of their cost economics. For the existing regional jet network we estimate the trade space of fuel and time for the replacement of regional jets with turboprops and find that all regional jet routes in China would generate savings if replaced with turboprops. We next establish future short-haul aviation routes between new and emerging airports and estimate the likelihood that a turboprop will be used. The finding that the most viable turboprop markets are spatially dispersed through the country validates considering turboprop investment at the state-level as a component of the established Chinese aviation sustainability initiative.     

Assessment of out-of-state truck activity in California

The continued growth of interstate freight trucking brings with it the potential for inaccuracies in official government statistics on trucks’ road use and pollutant emission contributions. State government agency emission estimates for states within the US commonly rely on in-state truck and fuel use records. Consequently, these records can incompletely reflect overall truck activity and emissions by neglecting the trucks that are registered and/or are fueled out of state. An in-person interview survey of 433 heavy-duty truck drivers was conducted at select points of entry into California to better understand the contribution of out-of-state trucks, their fuel use, and their emissions in California. The results indicate that non-California-registered trucks and non-California-diesel fuel make up approximately 30% of Class 8 heavy-duty truck mileage on California roads. Non-California-registered-truck mileage is disproportionately concentrated in four California air basins that have significant air quality issues.     

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.     

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.     

Joint implementation of tradable credit and road pricing in public-private partnership networks considering mixed equilibrium behaviors

This paper investigates joint road charging schemes in a public-private partnership (PPP) network by simultaneously taking into account Cournot-Nash (CN) players and user equilibrium (UE) players. Each joint scheme comprises a tradable credit plan for public roads and a regular tolling plan for private roads. We show that, under UE-CN mixed equilibrium, there exist anonymous nonnegative joint schemes that can support a system optimum link flow pattern. By using preemptive approach, we further design three bi-objective optimization models with hybrid implementation of tradable credit and road pricing. Numerical examples demonstrate that the proposed methods are effective in managing PPP networks.     

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.