Does urbanization lead to less energy use on road transport? Evidence from municipalities in Norway

The relationship between urbanization, energy use, and CO₂ emissions has been extensively studied in recent years, however little attention paid to the differences in urban forms. Previous studies implicitly assume that the urban form is homogenous across different urban areas. Such an assumption is questionable as urban form can have many different facets. This paper investigates the effects of urbanization on the road transport energy use by considering different urban forms from a dataset of 386 Norwegian municipalities from 2006 to 2009. Using the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model with an energy use identity equation, the main findings (1) confirm the well-established result that urban density has a negative and significant influence on road transport energy use, and (2) demonstrate that the effect of urbanization partly depends on the level of urban density. These results imply that additional increases in urbanization in dense areas yield greater decreases in road transport energy use per capita. Additional findings posit that (3) there is a non-linear (quadratic) relationship between road energy use per capita and urban population. This implies that an increase in total municipality population over a specific turning point can result in a decrease in road energy use per capita. However, (4) the ratio of urban residential buildings with private gardens has a negative and significant influence on road transport energy use. This implies that there may be a trade-off between compact and sprawl city development strategies, highlighting that sustainable energy use requires further investigation.     

Modeling and forecasting household energy consumption and related CO2 emissions integrating UrbanSim and transportation models: an Atlanta BeltLine case study

Reducing energy consumption and controlling greenhouse gas emissions are key challenges for urban residents. Because urban areas are complex and dynamic, affected by many driving factors in terms of growth, development, and demographics, urban planners and policy makers need a sophisticated understanding of how residential lifestyle, transportation behavior, land-use changes, and land-use policies affect residential energy consumption and associated CO₂ emissions. This study presents an approach to modeling and simulating future household energy consumption and CO₂ emissions over a 30-year planning period, using an energy-consumption regression approach based on the UrbanSim model. Outputs from UrbanSim for a baseline scenario are compared with those from a no-transportation-demand model and an Atlanta BeltLine scenario. The results indicate that incorporation of a travel demand model can make the simulation more reasonable and that the BeltLine project holds potential for curbing energy consumption and CO₂ emissions.     

The impact of land-use mix on residents' travel energy consumption: New evidence from Beijing

Policies that encourage mixed land use are widely believed to make transport more energy efficient. However, few studies have directly examined the impacts of land-use heterogeneity on travel energy consumption at the individual level. Moreover, the definition and measures of land-use heterogeneity are debated. This paper aims to fill these gaps using the large city of Beijing, China, as a case study. Three types of land use are examined in terms of their effects on individual residents’ travel energy consumption. The results suggest that high land-use diversity and a good jobs-housing balance significantly reduces commuting travel. Interestingly, highly heterogeneous retail and housing areas may have high travel energy use, as residents are more likely to go shopping. There are obvious spatial variations in these effects. Residents of suburban ‘newtowns’, where the jobs-housing balance is particularly good, consume less travel energy. The results suggest that decreased use of conventional planning patterns, such as the socialist danwei system, and increasing urban sprawl, bring new challenges to achieving transport efficiency. Mixed land-use policies can be an effective solution to these challenges.     

The impact of urban form on commuting in large Chinese cities

Studies on cities in Europe and the United States have demonstrated that travel behaviour is influenced by urban form. Based on these findings policies steering the shape of cities have been proposed to reduce urban transport emissions and limit congestion. Such policies can also be relevant for the rapidly growing and motorising Chinese cities. Yet, empirical evidence on the relationships between urban form and car usage is scarce for the specific Chinese context that is characterised by high densities, fast development and strong government steering. Using novel crowd-sourced datasets we study the impact of several urban form variables (city size, urban density, land-use mix, polycentricity and spatial clustering) on the cost of commuting expressed in time and distance. The results show that city size and spatial clustering are important determinants of commuting: large cities without clear clusters of businesses and other facilities have longer average commuting times and distances. Increased prosperity also adds to longer and lengthier commutes. Spatial planning measures that maintain or reinforce high-density clusters can help limiting commuting distance and time. Current sprawled urban development may have long-term, negative consequences for the accessibility and liveability of Chinese cities and could hamper their economic potential.     

Built environments and mode choice: toward a normative framework

Compact, mixed-use, and walk-friendly urban development, many contend, can significantly influence the modes people choose to travel. Despite a voluminous empirical literature, most past studies have failed to adequately specify relationships for purposes of drawing inferences about the importance of built-environment factors in shaping mode choice. This paper frames the study of mode choice in Montgomery County, Maryland around a normative model that weighs the influences of not only three core dimensions of built environments – density, diversity, and design – but factors related to generalized cost and socio-economic attributes of travelers as well. The marginal contributions of built-environment factors to a traditionally specified utility-based model of mode choice are measured. The analysis reveals intensities and mixtures of land use significantly influence decisions to drive-alone, share a ride, or patronize transit, while the influences of urban design tend to be more modest. Elasticities that summarize relationships are also presented, and recommendations are offered on how outputs from conventional mode-choice models might be “post-processed” to better account for the impacts of built environments when testing land-use scenarios.     

Transportation–land-use interaction: empirical findings in North America,and their implications for modeling

The last two decades have witnessed the implementation of various policies based on land-use to address problems stemming from automobile ownership and use. There are, however, questions with respect to the efficacy of such policies. This paper therefore reviews the literature on empirical studies of the transportation–land-use interaction with the objective of identifying the current state of knowledge concerning the interactions, particularly with respect to the impact land-use policies are likely to have on the system. The focus is on studies conducted in North America. The results are mixed; some studies conclude that urban densities, traditional neighborhood design schemes, and land-use mix have an impact on auto ownership and use. Other studies find the impact of such variables to be at best marginal. Gaps in our understanding of the interaction are identified. These are found to be primarily the result of data limitations and methodological weaknesses. A detailed discussion of the implications of the findings for the development and application of integrated transportation–land-use models is provided, with the recommendation that without such an integrated approach to analyzing the transportation–land-use interaction, any study of impacts of urban form on travel behavior is likely to yield erroneous results.     

Integrated analysis of land-use and transport policy interventions

Analysing the impact of urban policy interventions on urban growth, land use and transport (LUT) is crucial for urban planners, transport planners and policy-makers, especially in rapidly growing cities. This paper presents a cellular automata-based land-use/transport interaction model – Metronamica-LUTI – for Jeddah that is used to analyse the impact of different proposed policy interventions under two urban growth scenarios for the period 2011–2031. Used as an integrated policy impact assessment tool, the model demonstrates a strong reciprocal relationship between LUT in Jeddah. This study shows that relevant spatial information and integrated policy impact assessment can provide rich insights into the interaction between LUT, the appropriate policy to consider in place and time which traditional planning practice and typical static urban models cannot do.     

Evaluating unintended outcomes of regional smart-growth strategies: Environmental justice and public health concerns

Many urban areas are perusing infill, transit oriented, and other “smart-growth” strategies to address a range of important regional goals. Denser and more mixed use urban development may increase sustainability and improve public health by reducing vehicle travel and increasing the share of trips made by transit, walking and bicycling. Fewer vehicle trips results in fewer greenhouse gas and toxic vehicle emissions, and more trips made by walking and bicycle increases physical activity. Prior research has largely focused on modeling and estimating the potential size of these and other smart-growth strategy benefits. A largely overlooked area is the potential for unexpected public health costs and environmental justice concerns that may result from increasing density. We evaluate regional land-use and transportation planning scenarios developed for the year 2040 by a metropolitan planning organization with a newly developed regional air quality modeling framework. Our results find that a set of regional plans designed by the MPO to promote smart-growth that are estimated to result in less vehicle use and fewer vehicle emissions than a more typical set of plans results in higher population exposure to toxic vehicle emissions. The smart-growth plans also result in greater income-exposure inequality, raising environmental justice concerns. We conclude that a more spatially detailed regional scale air quality analysis can inform the creation of smarter smart-growth plans.     

To save money or to save time: Intelligent routing design for plug-in hybrid electric vehicle

Electric vehicles (EVs) are promising alternative to conventional vehicles, due to their low fuel cost and low emissions. As a subset of EVs, plug-in hybrid electric vehicles (PHEVs) backup batteries with combustion engines, and thus have a longer traveling range than battery electric vehicles (BEVs). However, the energy cost of a PHEV is higher than a BEV because the gasoline price is higher than the electricity price. Hence, choosing a route with more charging opportunities may result in less fuel cost than the shortest route. Different with the traditional shortest-path and shortest-time routing methods, we propose a new routing choice with the lowest fuel cost for PHEV drivers. Existing algorithms for gasoline vehicles cannot be applied because they never considered the regenerative braking which may result in negative energy consumption on some road segments. Existing algorithms for BEVs are not competent too because PHEVs have two power sources. Thus, even if along the same route, different options of power source will lead to different energy consumption. This paper proposes a cost-optimal algorithm (COA) to deal with the challenges. The proposed algorithm is evaluated using real-world maps and data. The results show that there is a trade-off between traveling cost and time consumed when driving PHEVs. It is also observed that the average detour rate caused by COA is less than 14%. Significantly, the algorithm averagely saves more than 48% energy cost compared to the shortest-time routing.     

Evaluating the impacts of urban corridor traffic signal optimization on vehicle emissions and fuel consumption

This study investigates the impacts of traffic signal timing optimization on vehicular fuel consumption and emissions at an urban corridor. The traffic signal optimization approach proposed integrates a TRANSIMS microscopic traffic simulator, the VT-Micro model (a microscopic emission and fuel consumption estimation model), and a genetic algorithm (GA)-based optimizer. An urban corridor consisting of four signalized intersections in Charlottesville, VA, USA, is used for a case study. The result of the case study is then compared with the best traffic signal timing plan generated by Synchro using the TRANSIMS microscopic traffic simulator. The proposed approach achieves much better performance than that of the best Synchro solution in terms of air quality, energy and mobility measures: 20% less network-wide fuel consumption, 8–20% less vehicle emissions, and nearly 27% less vehicle-hours-traveled (VHT).     

Modelling energy consumption of electric freight vehicles in urban pickup/delivery operations: analysis and estimation on a real-world dataset

Electric Freight Vehicles (EFVs) are a promising and increasingly popular alternative to conventional trucks in urban pickup/delivery operations. A key concerned research topic is to develop trip-based Tank-to-Wheel (TTW) analyses/models for EFVs energy consumption: notably, there are just a few studies in this area. Leveraging an earlier research on passenger electric vehicles, this paper aims at filling this gap by proposing a microscopic backward highly-resolved power-based EFVs energy consumption model (EFVs-ECM). The model is estimated and validated against real-world data, collected on a fleet of five EFVs in the city centre of Rome, for a total of 144 observed trips between subsequent pickup/delivery stops. Different model specifications are tested and contrasted, with promising results, in line with previous findings on electric passenger vehicles.     

Introducing a commute-energy performance index for Flanders

Based on 2001-census data for Belgium, energy consumption levels for commuting were calculated and mapped on the basis of residential locations in the administrative regions of Flanders and Brussels. Comparison with regional differentiations in commuting distances, modal shares of non-car travel modes and aspects of infrastructure and population densities clarifies some relationships between energy consumption, commuting behaviour and spatial-economic structure in the suburbanised historic–polycentric spatial structure which characterises the northern part of Belgium. It is found that mode choice appears to be of little impact for the energy performance of home-to-work travel on the scale of the Flanders region. At the other hand, proximity between home and work locations is paramount.Residential density plays a part in this, although much depends on the specific situation. This is also the case for the accessibility of the main road and rail network. In some regions these infrastructures induce long-distance commuting, whereas in the economic core areas this effect is much less pronounced. All these are factors that are very much determined by infrastructural and spatial policies of the past.     

The relationship between urban street networks and private motorized trips at the city level

There are factors that impact car usage in urban areas, such as density, diversity and design, but there have been few studies that examine the relationship between street network factors and car usage at the city level (macro level). This paper focuses on this relationship by introducing urban street network variables, such as blocks per area, nodes per block and length of roads and motorways, as independent variables and the percentage of daily trips by private motorized modes as the dependent variable. The street network in this study includes interconnecting lines and points that present streets, roads, motorways, intersections and blocks. The strength of the relationship in this study is found using multiple linear regression. The findings of this research indicate that an increase in car usage is correlated with an increasing number of blocks per area, number of nodes per block and length of motorways. In addition, because the urban street network is the result of macro-scale planning decisions, considering this relationship can lead to better planning decisions.     

Electric vehicles’ energy consumption measurement and estimation

Use of electric vehicles (EVs) has been viewed by many as a way to significantly reduce oil dependence, operate vehicles more efficiently, and reduce carbon emissions. Due to the potential benefits of EVs, the federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future EVs could gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: how to improve efficiency for EV transportation system? This research takes the first step in tackling this challenge by addressing a fundamental issue, i.e. how to measure and estimate EVs’ energy consumption. In detail, this paper first presents a system which can collect in-use EV data and vehicle driving data. This system then has been installed in an EV conversion vehicle built in this research as a test vehicle. Approximately 5 months of EV data have been collected and these data have been used to analyze both EV performance and driver behaviors. The analysis shows that the EV is more efficient when driving on in-city routes than driving on freeway routes. Further investigation of this particular EV driver’s route choice behavior indicates that the EV user tries to balance the trade-off between travel time and energy consumption. Although more data are needed in order to generalize this finding, this observation could be important and might bring changes to the traffic assignment for future transportation system with a significant share of EVs. Additionally, this research analyzes the relationships among the EV’s power, the vehicle’s velocity, acceleration, and the roadway grade. Based on the analysis results, this paper further proposes an analytical EV power estimation model. The evaluation results using the test vehicle show that the proposed model can successfully estimate EV’s instantaneous power and trip energy consumption. Future research will focus on applying the proposed EV power estimation model to improve EVs’ energy efficiency.     

Home-to-work commuting,urban form and potential energy savings: A local scale approach to regional statistics

The link between transport energy consumption and land use patterns has been the focus of a considerable amount of academic works over the past decades. While many empirical researches are backed up with solid statistical techniques, most of them do not fully consider the influence of scale underlying empirical quantitative investigations. Using fine-scale home-to-work commuting data for Wallonia (Belgium), this paper re-evaluates Breheny’s (1995) assertion that urban structure should hold the characteristics of major cities if substantial energy savings are to be achieved. A local scale approach highlights efficient settlements in terms of transport energy consumption not only within major towns, but also within remote rural areas. Furthermore, results suggest that influencing the urban form following local energy efficient examples rather than regional ones could also yield significant gains, without an extreme policy stance of re-urbanisation in major cities.     

Characteristics of speed dispersion and its relationship to fundamental traffic flow parameters

Speed dispersion is essential for transportation research but inaccessible to certain sensors that simply record density, mean speed, and/or flow. An alternative is to relate speed dispersion with these available parameters. This paper is compiled from nearly a quarter million observations on an urban freeway and a resulting data-set with two speed dispersion measures and the three fundamental parameters. Data are examined individually by lane and aggregately by direction. The first dispersion measure, coefficient of variation of speed, is found to be exponential with density, negative exponential with mean speed, and two-phase linear to flow. These empirical relationships are proven to be general for a variety of coefficient ranges under the above function forms. The second measure, standard deviation of speed, does not present any simple relationships to the fundamental parameters, and its maximum occurs at around a half to two-thirds of the free flow speed. Speed dispersion may be significantly different by lane.     

Modelling car ownership in Great Britain

Over the last 50 years there has been a tenfold increase in the number of cars in Great Britain, rising from 2.6 million vehicles in 1951 to 27 million vehicles in 2001. Over the same period there has been a steady reduction in the proportion of households without access to a car and a steady increase in the proportion of households with two or more cars. If such trends continue, it is likely that there will be increased energy consumption, increased problems with traffic congestion and atmospheric pollution, and reductions to the financial viability of public transport. Given the importance of car ownership to transport and land-use planning and its relationship with energy consumption, the environment and health, it is the objective of this research to develop econometric models of household car ownership and apply the models to generate forecasts across Britain to the year 2031. To achieve this objective, the research develops discrete choice models of the household’s decision to own zero, one, two or three or more vehicles as a function of market saturation, licence holding, household income and structure, household employment, company car provision, and purchase and use costs. The models are validated to data from the 2001 Census and are used to develop a range of forecasts taking into account changes to the socio-demographic characteristics of Britain.     

Travel demand in the US urban areas: A system dynamic panel data approach

Using panel data from 85 urban areas over a 20-year period and applying a system panel data approach, this paper examines the relationship between travel demand in terms of per-capita VMT and urban spatial characteristics. Regression results show that road density and urban spatial size have positive and statistically significant effects on travel demand in the US urban areas. Urban population density and urban congestion have negative and statistically significant effects on travel demand.     

An integrated assessment of factors affecting modal choice: towards a better understanding of the causal effects of built environment

This paper discusses the methodological challenges in understanding causal relationships between urban form and travel behavior and uses a holistic quasi-experimental approach to investigate the separable marginal influence of each of several urban form factors on mode choice as well as the complex relationships between those factors and a wide range of personal traits. Data analysis and models are used to reveal the effect of such interactions on mode choice for both work and non-work trips in Rome, Italy. It is found that population density does not have a significant marginal positive effect on sustainable mode choice for work trips. Conversely, this factor decreases sustainable mode choice for non-work trips. Small scale street design quality alone increases sustainable mode choice for non-work trips. This is while presence of street network integration alone increases automobile use for all trip purposes. The results point to the importance of incorporating all the urban form factors of diversity, design and street network integration if the goal is to increase the use of more sustainable modes of transportation for both work and non-work trips, but also show that attitudes and preferences can modify the response to urban design factors. The findings suggest that thoughtful policies triggering certain attitudes (cost sensitivity, sensitivity to peer pressure regarding the value attributed to sustainable transportation, and transit preference) can be adopted to significantly increase sustainable mode choice even in the neighborhoods with specific physical restrictions.     

Comparison of energy demands of drone-based and ground-based parcel delivery services

Drones are one of the most intensively studied technologies in logistics in recent years. They combine technological features matching current trends in transport industry and society like autonomy, flexibility, and agility. Among the various concepts for using drones in logistics, parcel delivery is one of the most popular application scenarios. Companies like Amazon test drones particularly for last-mile delivery intending to achieve both reducing total cost and increasing customer satisfaction by fast deliveries. As drones are electric vehicles, they are also often claimed to be an eco-friendly mean of transportation.In this paper an energy consumption model for drones is proposed to describe the energy demand for drone deliveries depending on environmental conditions and the flight pattern. The model is used to simulate the energy demand of a stationary parcel delivery system which serves a set customers from a depot. The energy consumed by drones is compared to the energy demand of Diesel trucks and electric trucks serving the same customers from the same depot.The results indicate that switching to a solely drone-based parcel delivery system is not worthwhile from an energetic perspective in most scenarios. A stationary drone-based parcel delivery system requires more energy than a truck-based parcel delivery system particularly in urban areas where customer density is high and truck tours are comparatively short. In rather rural settings with long distances between customers, a drone-based parcel delivery system creates an energy demand comparable to a parcel delivery system with electric trucks provided environmental conditions are moderate.