scholarly journals Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound

2019 ◽  
Author(s):  
Morteza Taiebat ◽  
Samuel Stolper ◽  
Ming Xu
Keyword(s):  
Travel Behavior ◽  
Energy Use ◽  
Travel Demand ◽  
Rebound Effect ◽  
Time Cost ◽  
Fuel Cost ◽  
Automated Vehicles ◽  
Time Costs ◽  
Induced Travel ◽  
The Impact

Connected and automated vehicles (CAVs) are expected to yield significant improvements in safety, energy efficiency, and time utilization. However, their net effect on energy and environmental outcomes is unclear. Higher fuel economy reduces the energy required per mile of travel, but it also reduces the fuel cost of travel, incentivizing more travel and causing an energy “rebound effect.” Moreover, CAVs are predicted to vastly reduce the time cost of travel, inducing further increases in travel and energy use. In this paper, we forecast the induced travel and rebound from CAVs using data on existing travel behavior. We develop a microeconomic model of vehicle miles traveled (VMT) choice under income and time constraints; then we use it to estimate elasticities of VMT demand with respect to fuel and time costs, with fuel cost data from the 2017 United States National Household Travel Survey (NHTS) and wage-derived predictions of travel time cost. Our central estimate of the combined price elasticity of VMT demand is -0.4, which differs substantially from previous estimates. We also find evidence that wealthier households have more elastic demand, and that households at all income levels are more sensitive to time costs than to fuel costs. We use our estimated elasticities to simulate VMT and energy use impacts of full, private CAV adoption under a range of possible changes to the fuel and time costs of travel. We forecast a 2-47% increase in travel demand for an average household. Our results indicate that backfire – i.e., a net rise in energy use – is a possibility, especially in higher income groups. This presents a stiff challenge to policy goals for reductions in not only energy use but also traffic congestion and local and global air pollution, as CAV use increases.

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

scholarly journals Housing Cost Dependence on Transport Accessibility Territory

2021 ◽  
Author(s):  
Sharov Maksim Igorevich
Keyword(s):  
Public Transport ◽  
Transportation Costs ◽  
Housing Cost ◽  
Time Cost ◽  
Transport Accessibility ◽  
Time Costs ◽  
Route Network ◽  
The Cost ◽  
The Impact ◽  
The City

The study the housing cost dependence on the transport accessibility of the territory of the city will improve the efficiency of the route network and will lead to a reduction in overall transportation costs. The research algorithm consisted of the following stages: data on transport accessibility were systematized; calculation of time costs assessed transport accessibility by city zones; the dependence of the cost per square meter on the time cost of movement is obtained. When assessing the impact of transport accessibility on the housing cost it is important to pay attention to the remoteness from the central part of the city, the proximity of highways, the system of access roads; the proximity of public transport stops with a large number of routes connecting different zones.

Analysis of the Effects of Connected–Automated Vehicle Technologies on Travel Demand

2017 ◽  
Vol 2625 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Joshua Auld ◽  
Vadim Sokolov ◽  
Thomas S. Stephens
Keyword(s):  
Traffic Flow ◽  
Travel Behavior ◽  
Energy Use ◽  
Travel Demand ◽  
Transportation System ◽  
Transportation Systems ◽  
Potential Range ◽  
Cruise Control ◽  
Automated Vehicle ◽  
Positive Effects

Connected–automated vehicle (CAV) technologies are likely to have significant effects not only on how vehicles operate in the transportation system, but also on how individuals behave and use their vehicles. While many CAV technologies—such as connected adaptive cruise control and ecosignals—have the potential to increase network throughput and efficiency, many of these same technologies have a secondary effect of reducing driver burden, which can drive changes in travel behavior. Such changes in travel behavior—in effect, lowering the cost of driving—have the potential to increase greatly the utilization of the transportation system with concurrent negative externalities, such as congestion, energy use, and emissions, working against the positive effects on the transportation system resulting from increased capacity. To date, few studies have analyzed the potential effects on CAV technologies from a systems perspective; studies often focus on gains and losses to an individual vehicle, at a single intersection, or along a corridor. However, travel demand and traffic flow constitute a complex, adaptive, nonlinear system. Therefore, in this study, an advanced transportation systems simulation model—POLARIS—was used. POLARIS includes cosimulation of travel behavior and traffic flow to study the potential effects of several CAV technologies at the regional level. Various technology penetration levels and changes in travel time sensitivity have been analyzed to determine a potential range of effects on vehicle miles traveled from various CAV technologies.

scholarly journals Safety Concerns and Travel Behavior of Generation Z: Case Study from the Czech Republic

2021 ◽  
Vol 13 (23) ◽  
pp. 13439
Author(s):  
Miroslav Rončák ◽  
Petr Scholz ◽  
Ivica Linderová
Keyword(s):  
Czech Republic ◽  
Travel Behavior ◽  
Travel Demand ◽  
Third Wave ◽  
The Czech Republic ◽  
Safety Concerns ◽  
Generation Z ◽  
One Year ◽  
People’S Attitudes ◽  
The Impact

Generation Z has been online since the beginning, the online space is an integral part of their lives and personalities, and they make up about 30% of the world’s population. It is claimed that this youngest cohort is already the most numerous generation on the Earth. The most important holiday parameters for them are price and location. They want to explore new places and be active while abroad. The study examines the impact of safety concerns on changes in travel behavior during the COVID-19 pandemic. We focused on members of Generation Z who study the Tourism and the Recreation and Leisure Studies programs, so these students have a positive attitude towards traveling. Data were collected via internal university systems at two periods of time connected to different stages of the pandemic outbreak. The sample was chosen randomly. The sample of Period 1 (n = 150) was composed in 2020, after the lifting of restrictions at the end of the first wave of the COVID-19 pandemic in the Czech Republic. The sample of Period 2 (n = 126) was collected one year later, after the lifting of restrictions at the end of the third wave of the COVID-19 pandemic in the Czech Republic. Correspondence analysis was used for better understanding and representation. This is a unique research study on Generation Z in the Czech Republic and Central Europe. As a result of the contemporary demographic changes in the world, this generation will shape future travel demand. Hence, understanding these youngest travelers will be key to predicting how tourism trends could evolve in the next few years and how these could influence worldwide tourism. The respondents thought they would not change their travel habits in the next five years because of the pandemic. When Periods 1 and 2 were compared after one year of the pandemic, the respondents preferred individual trips to group trips and individual accommodation to group accommodation facilities. On the other hand, our findings revealed a significant increase in safety concerns related to changes in travel behavior when the above-mentioned periods were compared. The research contributes to mapping young people’s attitudes towards travel in the constrained and changing conditions resulting from the COVID-19 pandemic. The findings help analyze the consumer behavior of the target group.

scholarly journals MODELING AND ESTIMATING THE CAPACITY OF URBAN TRANSPORTATION NETWORK WITH RAPID TRANSIT

Transport ◽  
2014 ◽  
Vol 29 (2) ◽  
pp. 165-174 ◽  
Author(s):  
Lin Cheng ◽  
Muqing Du ◽  
Xiaowei Jiang ◽  
Hesham Rakha
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Transportation Network ◽  
Urban Transportation ◽  
Network Capacity ◽  
Rapid Transit ◽  
Demand Model ◽  
Trip Distribution ◽  
Capacity Model ◽  
The Impact

To study the impact of the rapid transit on the capacity of current urban transportation system, a two-mode network capacity model, including the travel modes of automobile and transit, is developed based on the well-known road network capacity model. It considers that the travel demand accompanying with the regional development will increase in a variable manner on the trip distribution, of which the travel behavior is represented using the combined model split/trip distribution/traffic assignment model. Additionally, the choices of the travel routes, trip destinations and travel modes are formulated as a hierarchical logit model. Using this combined travel demand model in the lower level, the network capacity problem is formulated as a bi-level programming problem. The latest technique of sensitivity analysis is employed for the solution of the bi-level problem in a heuristic search. Numerical computations are demonstrated on an example network, and the before-and-after comparisons of building the new transit lines on the integrated transportation network are shown by the results.

THE EFFECT OF STRESS TOLERANCE ON DYNAMICS OF ACTIVITY–TRAVEL BEHAVIOR: NUMERICAL SIMULATION RESULTS

2014 ◽  
Vol 17 (06) ◽  
pp. 1450027 ◽  
Author(s):  
IFIGENIA PSARRA ◽  
THEO ARENTZE ◽  
HARRY TIMMERMANS
Keyword(s):  
Stress Tolerance ◽  
Travel Behavior ◽  
Dynamic Models ◽  
Travel Demand ◽  
Face Validity ◽  
The Face ◽  
Short And Long Term ◽  
Typical Day ◽  
The Impact

The primary and secondary effects of various spatial and transportation policies can be evaluated with models of activity–travel behavior. Whereas existing activity-based models of travel demand simulate a typical day, dynamic models simulate behavioral response to endogenous or exogenous change, along various time horizons. The current study aims at developing a model of endogenous dynamics of activity–travel behavior. Endogenous dynamics are induced by stress, which is regarded as dissatisfaction with current habits. It is assumed that people try to alleviate stress by trying short-term changes, within the options known to them or by exploring new options. If these explorations prove to be unsuccessful, they will consider long-term changes, such as moving to a new residential location, buying a car, etc. Therefore, this self-improvement process can result in both short and long-term adaptations. In the proposed framework, choice-set formation is modeled, the key concepts of aspiration, activation, awareness and expected utility are integrated, while both rational and emotional mechanisms are taken into account. Numerical simulations are conducted in order to check the face validity of the model, as well as the impact of stress tolerance parameters on system performance.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

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