Travel Energy Consumption Research Articles

Investigating the Impacts of Urban Built Environment on Travel Energy Consumption: A Case Study of Ningbo, China

The built environment of cities has increasingly affected the travel mode of residents and led to changes in energy consumption, which is crucial to maintaining urban sustainability. Ningbo is a typical representative of urbanization on the east coast of China, and its energy consumption is in a period of rapid growth. Therefore, using the survey data of 22,112 traffic trip samples from nine streets in Ningbo, this paper establishes a regression analysis model, systematically analyzes the relationship between the built environment and domestic energy consumption from multiple dimensions, and reveals the impact mechanism of the built environment on domestic energy consumption. We find that (1) social and economic conditions are the main factors affecting traffic energy consumption. (2) The population density has a significant negative correlation effect on the energy consumption of transportation trips. When the population density increases by 1%, the energy consumption of total transportation trips, commuting trips, high-energy-consumption trips, and low-energy-consumption trips decreases by 0.094%, 0.115%, 0.273%, and 0.124%, respectively. (3) When the degree of mixed use of land increases by one percentage point, the energy consumption of total transportation trips, commuting trips, high-energy-consumption trips, and low-energy-consumption trips decreases by 0.415%, 0.421%, 2.574%, and 1.197%, respectively. (4) The density of road intersections has a significant negative correlation effect on the energy consumption of traffic trips. (5) The impact of the built environment on the energy consumption of transportation trips is greater than that of residential buildings.

Investigating the Impacts of Urban Built Environment on Travel Energy Consumption: A Case Study of Ningbo, China

Investigating the Impacts of Urban Built Environment on Travel Energy Consumption: A Case Study of Ningbo, China

A vehicle-cloud collaboration strategy for remaining driving range estimation based on online traffic route information and future operation condition prediction

Due to the complexity of real driving and operating conditions of Battery Electric Vehicles, it is challenging to accurately estimate the remaining driving range of the vehicle. Relying only on traditional energy consumption prediction based on the historical data shows obvious low-fidelity and hysteresis, especially when the traffic route is unknown. The accuracy of future travel energy consumption prediction fails to be guaranteed once the switching of operating conditions is involved. For this reason, a map named “Driving Route Planning” Application Programming Interface server is built on the cloud, receiving online traffic route information, and the Hidden Markov Model is applied for prediction optimization of future operating conditions. The remaining driving range of Battery Electric Vehicles is finally estimated according to the future energy consumption and the remaining dischargeable energy. The results show that the prediction of operating condition combined with traffic route information and Hidden Markov Model reflects the switching of future operating conditions more accurately and quickly. The relative error of the remaining driving range estimation proposed keeps within 5% under the real operating verification.

In-Company Smart Charging: Development of a Simulation Model to Facilitate a Smart EV Charging System

Current electric vehicle (EV) charging systems have limited smart functionality, and most research focuses on load-balancing the national or regional grid. In this article, we focus on supporting the early design of a smart charging system that can effectively and efficiently charge a company’s EV fleet, maximizing the use of self-generated Photo-Voltaic energy. The support takes place in the form of the Vehicle Charging Simulation (VeCS) model. System performance is determined by operational costs, CO2 emissions and employee satisfaction. Two impactful smart charging functions concern adaptive charging speeds and charging point management. Simulation algorithms for these functions are developed. The VeCS model is developed to simulate implementation of a smart charging system incorporating both charging infrastructure and local Photo-Voltaics input, using a company’s travel and energy data, prior to having the EVs in place. The model takes into account travel behaviour, energy input and energy consumption on a daily basis. The model shows the number of charged vehicles, whether incomplete charges occur, and energy flow during the day. The model also facilitates simulation of an entire year to determine overall cost and emission benefits. It also estimates charging costs and CO2 emissions that can be compared to the non-EV situation. With the VeCS model, the impact of various system design and implementation choices can be explored before EVs are used. Two system designs are proposed for the case company; a short-term version with current technology and a future version with various smart functionalities. Overall, the model can contribute to substantiated advice for a company regarding implementation of charging infrastructure.

Energy consumption and emissions dynamics of US domestic intercity air travel

• Estimates energy usage and emissions of domestic intercity air travel in the U.S. • Analyzes emissions dynamics of intercity air travel via supply and demand factors. • Emissions per passenger have declined by nearly 40% over the last two decades. • Route frequency, fleet efficiency and deployment are leading contributing factors. Domestic intercity air travel accounts for nearly 80% of all air travel in the U.S. As low-cost airlines and economic growth have increased the affordability of intercity air travel, the benefits to travelers and cities are touted by most. Yet, an accounting of the energy consumption and emissions from domestic intercity air travel is rarely discussed. This paper fills that gap and provides an analysis of the trends in fuel consumption and emissions by intercity air travel as well as an analysis of the contributing factors to these emissions. Using flight tracking and carrier data, emissions per passenger are found to have declined by 40% over the past two decades while total emissions have increased by 10% over the past 8 years. A decomposition analysis of the market factors highlights changes in route frequency, fleet deployment, and fleet efficiency as the leading factors contributing to the decline of intercity emissions.

An Attention-Based Model for Travel Energy Consumption of Electric Vehicle with Traffic Information

Mileage anxiety is one of the most important factors that affect the driving experience due to the limitation of battery capacity. Robust and accurate prediction of the energy consumption of the journey of the electric vehicle can guide the driver to allocate the power rationally and relieve the anxiety of the mileage. Since vehicle sharing is the biggest application scenario of electric vehicles, it is a critical challenge in share mobility research area. In this paper, a travel energy consumption prediction model of electric vehicles is proposed in order to improve the mobility of shared cars and reduce the anxiety of drivers because they are worried about insufficient power. A recurrent neural network with attention mechanism and deep neural network is used to build the model. To validate the proposed model, a simulation is demonstrated based on both traffic and vehicle information. After the simulation, experimental results show that the proposed model has high prediction accuracy, and we also show through visualization how the model finds high relevant road segments of the road network while dealing with corresponding traffic state input.

Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

Speed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

Planning of Charging Facilities Considering Terrain Characteristics

The existing studies on the planning of charging facilities generally neglect the influence of terrain characteristics. First, this study quantifies the transportation characteristics based on the relative height to calculate the travel energy consumption of electric vehicles (EVs) users. Then, the charging demand of EVs is estimated and assigned according to urban traffic flow. Finally, a land purchasing coefficient is proposed based on a functional agglomeration phenomenon. The coefficient is integrated into formulating a planning model with the objective of minimising the total annual social costs. Numerical experiments are conducted to validate the novel planning method. The experimental results verify that the terrain characteristics increase users’ travel energy consumption and limit their driving range.

Multimodal Traveller Information System using both Web-Based Design Program and Traffic Analysis for Akure Metropolis

A Traveller’s Information System integrated with traffic condition which can provide vital information to the users of the system is such a powerful tool in transportation planning. In this study, web-based multimodal advanced traveller information system was developed, which supplies information to trip makers within Akure metropolis and major cities with similar traffic condition. Traffic count was also taken on five roads which most of the transportation company’s vehicle ply so as to check for the traffic condition of the roads. The system provide information such as the available route from the origin of the traveller to his or her destination, the travel time, distance of each route, closest facilities like hotels, hospitals, airline services, airport terminal, departure time, departure time, arrival time and fare. The traffic count carried out revealed that Oyemekun road has the highest volume of traffic followed by Akure-Ado road while FUTA North gate road gives the least volume of traffic. The reason for the highest volume of traffic recorded at Oyemekun road is as a result of it location close to the CBD (Central Business District) while the lowest volume of traffic recorded for FUTA North gate is as a result of it location almost at the outskirt of the town. The web-based multi-modal traveller’s information system developed will help trip makers in Akure metropolis minimize the cost of their trips, by reducing travel time, energy consumption, and transport fare. Travellers are advised to embark on their journey earlier than the peak periods so as to avoid delay.

A Multimodal Traveller Information System using both Web-Based Design Program and Traffic Analysis for Akure Metropolis

A Traveller’s Information System integrated with traffic condition which can provide vital information to the users of the system is such a powerful tool in transportation planning. In this study, web-based multimodal advanced traveller information system was developed, which supplies information to trip makers within Akure metropolis and major cities with similar traffic condition. Traffic count was also taken on five roads which most of the transportation company’s vehicle ply so as to check for the traffic condition of the roads. The system provide information such as the available route from the origin of the traveller to his or her destination, the travel time, distance of each route, closest facilities like hotels, hospitals, airline services, airport terminal, departure time, departure time, arrival time and fare. The traffic count carried out revealed that Oyemekun road has the highest volume of traffic followed by Akure-Ado road while FUTA North gate road gives the least volume of traffic. The reason for the highest volume of traffic recorded at Oyemekun road is as a result of it location close to the CBD (Central Business District) while the lowest volume of traffic recorded for FUTA North gate is as a result of it location almost at the outskirt of the town. The web-based multi-modal traveller’s information system developed will help trip makers in Akure metropolis minimize the cost of their trips, by reducing travel time, energy consumption, and transport fare. Travellers are advised to embark on their journey earlier than the peak periods so as to avoid delay.

Causal Effect of Time-Use Behavior on Residential Energy Consumption in China

Energy is essentially consumed to support residents' productive and consumptive activities arranged in their timeline. Hence, analyzing residential energy consumption from the perspective of how people use their time for participating different activities would provide more underlying insights. However, the relationship between time-use behavior and energy consumption has not been comprehensively and accurately quantified. Here, we go beyond pervious literature to investigate the causal effect from the initial generation of activity to the final energy consumption and explore the potential energy changes in the residential sector by shedding light on residents' time-use behavior. To fulfill this research, we improve the time-use survey framework used worldwide by including the equipment dimension. The results show that the average residential energy consumption in a Chinese household was approximately 1570.5 kgce in 2015, among which cooking consumed most of the energy. Through promoting energy-efficient equipment and eliminating the equipment usage during non-primary activities would lead to 3.5–13.2% energy reductions; however, the increasing popularity of online activities and services (E-life) might increase the residential energy consumption by approximately 18.1% even after avoiding the energy consumption of related travel.

Linking Travel Behavior and Urban Form with Travel Energy Consumption for Kathmandu Valley, Nepal

Abstract Escalating transport energy demand is one of the major issues for Kathmandu Valley, with a rapid increment in automobilization and urban sprawl. The Kathmandu Valley is the center for econ...

Impact of urban development on residents’ public transportation travel energy consumption in China: An analysis of hydrogen fuel cell vehicles alternatives

Urban development has an important influence on the energy consumption of transportation. To develop public transportation is one of the important ways to decrease the energy consumption of urban transportation. It is very urgent to upgrade technology to reduce the energy consumption and emissions of the vehicles constantly. The popularization of hydrogen fuel cell vehicles is the trend of the future automobile industry, which can effectively reduce traffic energy consumption and alleviate urban pollution. This article analyzes the impact of urban development on public transport and private transportation energy consumption from 2013 to 2015; and uses hydrogen fuel cell vehicles alternatives in urban public transport as a scenario. It shows that urban economic development can effectively reduce public transport. Population growth will increase greatly energy consumption of public transport, while larger cities with reasonable spatial density can reduce traffic energy consumption. Moreover, hydrogen fuel cell vehicles can effectively reduce the energy consumption and pollution emissions of urban transportation during operating. Based on the above conclusions, this article will eventually provide targeted recommendations for the development of Chinese cities, public transport, and hydrogen fuel cell vehicles.

An improved algorithm for dispatching the minimum number of electric charging vehicles for wireless sensor networks

The very limited sensor battery energy greatly hinders the large-scale, long-term deployments of wireless sensor networks. This paper studies the problem of scheduling the minimum charging vehicles to charge lifetime-critical sensors in a wireless rechargeable sensor network, by utilizing the breakthrough wireless charging technology. Existing studies still employ a number of charging vehicles to charge sensors. The purchase cost of a charging vehicle however is not inexpensive. To further reduce the number of employed charging vehicles, we propose a novel approximation algorithm, by exploring the combinatorial properties of the problem. The techniques exploited in this paper are essentially different from that in existing studies. Not only do we show that the approximation ratio of the proposed algorithm is much better than that of the state-of-the-art, but also extensive experimental results demonstrate that the number of scheduled charging vehicles by the proposed algorithm is at least 10% less than that by the existing algorithms and the total travel energy consumption of the charging vehicles is also smaller than that by the existing algorithms.

Influencing Mechanism Analysis of Urban Form on Travel Energy Consumption—Evidence from Fukuoka City, Japan

The increasing threat of transportation energy insecurity, environmental issues and public health issues have led to a growing body of research that looks at the potential contribution of urban planning in reducing travel energy consumption. The sustainable planning strategies aimed at reducing travel energy consumption need to understand the relationships between urban form, travel behavior and energy consumption. This study provided additional insights into the relationships between urban form, travel mode choice and energy consumption via mode-wise (non-motorized, motorcycle, car, bus and rail) stratified models and travel energy consumption model by applying the multiple linear regression model based on 108 zones of Fukuoka city, Japan. This study suggests that urban form makes a major contribution towards conserving travel energy in cities. This study shows that the provision of bus stops and rail stations are essential alongside an increase in road connectivity, otherwise increasing only road connectivity encourages people to use private modes of transport, which is not good for travel energy conservation. The findings suggest that better transit accessibility promotes public mode use, and reduces private mode use and travel energy consumption even where travel distance is longer. Higher density with greater land use mix and better transit accessibility has a more profound influence on increasing use of the non-motorized mode.

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.

ERouting: An Eco-Friendly Navigation Algorithm for Traffic Information Industry

This study proposes an eco-friendly navigation algorithm, eRouting, to save energy and reduce CO $_{2}$ emission. The important research issue of traffic information industry, eco-friendly navigation, has been widely studied. As an improvement, in this paper, combining real-time traffic information and a representative factor-based energy/emission model, a calculated route is dynamically adjusted during the travel of a vehicle. eRouting is a centralized algorithm. It profits from the following aspects to achieve improved performance: a representative factor-based energy/emission model, a real-time traffic information-based dynamic adjustment, and an objective function to control the optimization direction to optimize the final energy consumption of vehicle's travel. As a peculiarity of this paper, the design of the representative factor-based model mines the impact of road-level parameters on energy consumption and CO $_{2}$ emission. Such mining is helpful to improve the pertinence of a model by formulating the key influence factors into the model. Experimental results are presented to prove the validity of eRouting. In addition, by contrast experiments, eRouting shows improved performance compared with an eco-friendly navigation algorithm and three traditional navigation algorithms.

Modelling household travel energy consumption and CO2 emissions based on the spatial form of neighborhoods and streets: A case study of Jinan, China

This paper presents a direct modelling approach to evaluate household travel energy consumption and CO2 emissions based on the spatial form of neighborhoods and streets. It integrates one multinomial logit model and four double hurdle models to predict travel outcomes: vehicle ownership portfolio choice, car travel distance, bus travel distance, motorcycle travel distance and ebike travel distance. The energy consumption and CO2 emissions are then estimated using these travel outcomes. A full application of the modelling approach is demonstrated through a pilot project in Jinan, China. A holdout validation is also performed to address the over-fitting problem of models calibrated from the training set data. Results show that the proposed approach is operational and appropriate for sketch planning applications to promote clean energy and low carbon city planning in urbanizing China.

Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

Trends in Automobile Energy use and GHG Emissions in Suburban and Inner City Neighborhoods: Lessons from Metropolitan Phoenix, USA

This paper compares the effects of long term suburban growth on travel behavior, energy consumption, and GHG emissions through a case study of neighborhoods in central Phoenix and the suburban city of Gilbert, in the Phoenix metropolitan region, USA. Motorized travel patterns in these study areas are estimated using 2001 and 2009 National Household Travel Survey data. Energy consumption and GHG emissions, including both Carbon Dioxide and Nitrous Oxide for each study area are estimated based on the corresponding trip distribution results. The final normalized outcomes are compared spatially between Phoenix and Gilbert within the same year, and temporally between year 2001 and 2009 to determine how the differential land use changes in those places influenced travel energy consumption and GHG emissions.