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Potential Implications of Automated Vehicle Technologies on Travel Behavior and System Modeling
Abstract
Autonomous Vehicles (AVs) are computer equipped vehicles that can operate without human driver’s active control using information provided by their sensors about the surrounding environment. Self-driving vehicles may have seemed to be a distant dream several years ago, but manufactures’ prototypes showed that AVs are becoming real now. Several car manufactures (i.e. Benz, Audi, etc.) and information technology firms (i.e. Google) have either showcased their fully AVs or announced their robot cars to be released in a few years. AVs hold the promise to transform the ways we live and travel. Although several studies have been conducted on the impacts of AVs, much remains to be explored regarding the various ways in which AVs could reshape our lifestyle.
This dissertation addresses the knowledge gap in understanding the potential implications of AV technologies on travel behavior and system modeling. A comprehensive review of literature regarding AV adoption, potential impacts and system modeling was provided. Bass diffusion models were developed to investigate the market penetration process of AVs based on experience learned from past technologies. A stated preference survey was conducted to gather information from university population on the perceptions and attitudes toward AV technologies. The data collected from the Florida International University (FIU) was used to develop econometric models exploring the willingness to pay and relocation choices of travelers in light of the new technologies. In addition, the latest version of the Southeast Planning Regional Model (SERPM) 7.0, an Activity-Based Model (ABM), was employed to examine the potential impacts of AVs on the transportation network. Three scenarios were developed for short-term (2035), mid-term (2045) and long-term (2055) conditions.
This dissertation provides a systematic approach to understand the potential implications of AV technologies on travel behavior and system modeling. The results of the survey data analysis and the scenario analysis also provide important inputs to guide planning and policy analysis on the impacts of AV technologies.
... The cost of using a car is increased by 10 % to demonstrate the increase in the travel cost due to the parking pricing as well as to motivate people to use the P&R system and switch to public transport. The marginal utility of traveling by AV is 60 % of the conational car's, as shown by previous studies (Steck et al., 2018;Bozorg, 2016). The simulation is run, and the results present changes in the existing modal share and in the travel measures like time and distance. ...
Travelers' behavior is predicted based on their individual preferences. People search for alternatives to maximize their benefit from doing activities, such as increasing the activity time by minimizing the travel time. Traffic congestion and the scarcity of parking spaces in the city center motivate the decision-makers to encourage travelers to use the park-and-ride (P&R) system. An evaluation concerning the impact of using the P&R system on the travel behavior of car users is conducted. Some of the existing P&R facilities are incorporated into the daily activity plans of car travelers to produce new daily activity plans (i.e., P&R facility is considered an activity). By using the Multi-Agent Transport Simulation (MATSim) open-source tool, simulations of the daily activity plans including the P&R system and autonomous vehicles (AVs) are conducted. The study examines three scenarios: (1) a simulation of the existing condition, (2) a simulation of the daily activity plans of the travelers with the P&R system, and (3) a simulation of the daily activity plans of the travelers with the P&R system and AVs. The results show that using the P&R system increases the overall travel time compared with the existing conditions, and the use of AVs as a transport mode impacts the existing modal share as follows: 64 % of the car users switch to AVs, while 15 % of the car users switch to public transport. The output of this study might be used by policy-makers in parking pricing and the location of the P&R facilities.
Automated vehicles (AV) are undergoing
development at a remarkable pace and have the
potential to revolutionize the existing transportation
system. The ASCE [1] evaluated the United States’
infrastructure as a D+ grade. Moreover, they
predicted radical infrastructure investment gaps in
the surface transportation sector in the upcoming
years. Some new urbanized regions might require
new highways. Meanwhile, many other highways are
reaching the end of their service life and will need
significant repairs or even replacement. However,
this seems to be unrealistic to happen until having a
high market penetration of Fully Connected and
Autonomous vehicles on the road to benefit from the
capacity expansion benefits. Regarding the funding
related issues of highway construction in the U.S.
and the emergence of AVs, having a better
understanding of the future’s traffic status is a must.
This study investigates the impact of autonomous
vehicles on Florida’s district five of I-95 highway
traffic including three counties: Flagler, Volusia, and
Brevard. This research is the first study to develop a
fusion model considering the impact of both traffic
flow and capacity adjustments based on the
literature review to forecast the traffic from 2020 to
2040 by considering the increasing AV market
penetration. The proposed approach provides a
more realistic plan for government agencies and
private investors, and as a result, significant savings
financially and resource-wise can be achieved. The
findings of the study confirm that autonomous
vehicles will increase traffic flow and capacity, and
the increase in flow is higher than the increase in
capacity.
From early studies of time allocation onward, it has been acknowledged that the “productive” nature of travel could affect its utility. At the margin, an individual may choose transit over a shorter automobile trip, if thereby she is able to use the travel time more productively. Alternatively, the recent advancements toward partly/fully automated vehicles are poised to revolutionize the perception and utilization of travel time in cars, and are further blurring the role of travel as a crisp transition between location-based activities.
To quantify these effects, we created and administered a survey to measure multitasking attitudes and behavior while commuting, together with general attitudes, mode-specific perceptions, and standard socioeconomic traits (N = 2120 Northern California commuters). We present a revealed preference mode choice model that accounts for the impact of multitasking attitudes and behavior on the utility of various alternatives. We find that engaging in productive activities (i.e. electronic reading/writing and using a laptop/tablet) significantly influences utility and could account for a small but non-trivial portion of the current mode shares. For example, the model estimates that commuter rail and car/vanpool shares would respectively be 0.38 and 3.22 percentage points lower, and the drive-alone share 3.00 percentage points higher, if the option to use time productively while traveling were not available. Conversely, in a hypothetical autonomous vehicles scenario, where the car would allow a high level of engagement in productive activities, driving alone and car/vanpool shares increased by 0.95 percentage points and 1.08 percentage points, respectively.
Automobile manufacturers, transportation researchers, and policymakers are interested in knowing the future of connected and autonomous vehicles (CAVs). To this end, this study proposes a new simulation-based fleet evolution framework to forecast Americans’ long-term (year 2015–2045) adoption levels of CAV technologies under eight different scenarios based on 5% and 10% annual drops in technology prices; 0%, 5%, and 10% annual increments in Americans’ willingness to pay (WTP); and changes in government regulations (e.g., mandatory adoption of connectivity on new vehicles). This simulation was calibrated with data obtained from a survey of 2167 Americans, regarding their preferences for CAV technologies (e.g., WTP) and their household’s annual vehicle transaction decisions.
This paper presents an effort to develop a market penetration model for autonomous vehicle (AV) technology adoption on the basis of similar technologies and previous trends in the United States. Generalized Bass diffusion models are developed on the basis of data from earlier technologies, including sales and price data on conventional automobiles and hybrid electric vehicles, and the use of Internet and cell phones. On the basis of the adoption patterns of earlier technologies, two values that represented the innovation factor (i.e., risk-taking capacity) and the imitation factor (i.e., culture and lifestyle preferences) were selected for AV market penetration. In addition, external variables (e.g., price of AVs compared with price of conventional vehicles) and economic wealth were incorporated into the model. The market size for AV adoption was determined on the basis of the use of the Internet, and a household was considered as the unit. Given the uncertainties in market size and the price of AVs, sensitivity analyses were conducted to understand the possible impacts of these factors on user adoption. In general, a larger market size led to a higher adoption rate, although the initial cost of AVs compared with that of conventional vehicles did not seem to influence the diffusion process much. This paper contributes to the literature through the addition of a quantitative analysis of AV market penetration on the basis of earlier technology adoption experience. The study results provided valuable insights in terms of possible market diffusion patterns and the impacts of different factors on user adoption.
The goal of this research is to investigate the factors that affect carsharing demand. As a proxy for carsharing demand, the number of (booking) transactions made by carsharing users is counted based on the data from one of the two major carsharing operators in Seoul, Korea. In order to identify the factors influencing station-based carsharing usage, multiple linear regression modeling was performed with the number of carsharing transactions as a dependent variable and with the three groups of independent variables: Built environment, demographic, and transportation variables. Instead of using the locations of the pods, this study uses the residential locations of carsharing users who made transactions, and the final result analyzing 420 districts shows that six variables significantly influence carsharing usage. Carsharing demand is high in an area where a higher proportion of building floor area is used for business, and which has a higher proportion of young residents in their 20s and 30s. It can also be predicted that the area with more registered cars and less subway entrances will show higher carsharing demand. The analysis result also suggests that providing additional carsharing pods, especially pods that utilize city owned public parking facilities, will help promote carsharing usage. This research establishes a basis for future research efforts to forecast carsharing demand and to identify areas with high potential, especially in major Asian cities.
Gives a unique up-to-date account of mainstream approaches to transport modelling with emphasis on the implementation of a continuous approach to transport planning. The authors discuss modern transport modelling techniques and their use in making reliable forecasts using various data sources. Importance is placed on practical applications, but theoretical aspects are also discussed and mathematical derivations outlined. -from Publisher
Since the publication of the Bass model in 1969, research on the modeling of the diffusion of innovations has resulted in a body of literature consisting of several dozen articles, books, and assorted other publications. Attempts have been made to reexamine the structural and conceptual assumptions and estimation issues underlying the diffusion models of new product acceptance. The authors evaluate these developments for the past two decades. They conclude with a research agenda to make diffusion models theoretically more sound and practically more effective and realistic.
This presentation aimed to introduce a comprehensive list of effective factors on traffic safety in urban areas, from a transportation planning perspective.
Traffic safety policies are still conventionally ranked on financial and environmental criteria in developing countries, yet the equity concept can be advantageously used as an integral part of the process of traffic safety policy making. Equity in transportation is defined as how appropriately and equally the impacts of transportation are distributed among different types of users.
The main contribution of this research is to introduce traffic safety equity in transportation policy making. We advocate for a modeling framework that considers traffic safety equity as a focal point. The Analytical Network Process overcomes the drawbacks of data intensive models that are so difficult to implement reliably in developing countries and recognizes the existence of complex interdependencies among traffic safety factors. The approach is demonstrated on the case of Tehran, Iran.
In this study, seventeen transportation elements are grouped into four clusters (driver characteristics, roadway characteristics, vehicle characteristics and traffic control) to evaluate six policies. Public education and information is found to be the most effective policy to increase traffic safety, considering equity. The best equity-conscious policies are independent from physical infrastructures, household income level and household location. The proposed framework is a sound approach to introduce traffic safety equity in the traffic safety policy making process of developing countries.
Forecasting the potential demand for electric vehicles is a challenging task. Because most studies for new technologies rely on stated preference (SP) data, market share predictions will reflect shares in the SP data and not in the real market. Moreover, typical disaggregate demand models are suitable to forecast demand in relatively stable markets, but show limitations in the case of innovations. When predicting the market for new products it is crucial to account for the role played by innovation and how it penetrates the new market over time through a diffusion process. However, typical diffusion models in marketing research use fairly simple demand models. In this paper we discuss the problem of predicting market shares for new products and suggest a method that combines advanced choice models with a diffusion model to take into account that new products often need time to gain a significant market share. We have the advantage of a relatively unique databank where respondents were submitted to the same stated choice experiment before and after experiencing an electric vehicle. Results show that typical choice models forecast a demand that is too restrictive in the long period. Accounting for the diffusion effect, instead allows predicting the usual slow penetration of a new product in the initial years after product launch and a faster market share increase after diffusion takes place.