Auto Ownership Affected by Automated Vehicles - Essay Example

Unit Lecturer Definition of Autonomous driving technology Autonomous vehicle technologies is defined as those technologies that gather information concerning the environment and autonomously make adjustments about driving to alert the driver to take partial control of the vehicle or to drive the car autonomously without any human control. The Impact of Autonomous Vehicle Technology on Operations at Major Public and future speculation On one hand, higher levels of highway capacity and low levels of parking requirements will remove some of the benefits of transit in dense urban environments, but conversely automated vehicles could help to reduce low-volume bus routes, providing great service to customers and lower costs operations for the transit agency. Automation will also help safer transit-3 vehicle operation, potentially resulting in high cost savings because to reduced self-insured losses. In addition, partial automation in bus vehicles may lead to highly reduced headways and thus increased people-moving capacities in environments where capacity is a constraining factor. Impacts and independent future speculations This part consists an analysis of the possible effects of the implementation of autonomous vehicles on the society. Modern transportation has a very significant role in the world. Transportation is a very fast growing sector, which is greatly associated with new technologies. In this time, the technology is evolving so fast that it is hard for people to get used to it. Making educated speculations concerning the future developments and determining their possible findings helps people understand and prepare for these variations. This is why it is significant to determine possible results of the implementation of autonomous vehicle technologies. This part will elaborate on the socio-economic effects of autonomous vehicles. Safety Safety matters have the most critical impact on daily life of all the transportation problems. Accidents from the traffic sector have colossal negative impacts on economy. For instance, in the European Union, there are over 40,000 accidents with about 1.3 million accidents annually. Every life lost through traffic accident results to a very high financial cost to the community as well as its appalling social impacts on people. Community’s intelligence, work-force together with social values is lost with the people dying in traffic accidents. Injuries too have huge financial effects, because treatment expenses are very high and the injured individuals are unable to work for a given of time. The most efficient solution to these accidents is the implementation of much better intelligent vehicle safety systems which will gradually evolve into fully autonomous vehicles. In the long run the implementation of autonomous vehicles seems to be a very profitable investment. An economical analysis carried out on a recent European project called “eCall” depicts how intelligent systems can save the economy. The eCall project aims at implementing a special emergency system on every car Impacts on traffic, economy and society Introduction of a fully autonomous vehicle in the transport system, traffic flow would immediately change. Traffic is presently a nuisance to drivers almost all over the world. The average person in the United States waited for 26 hours in traffic during the whole year in 2001. This is a very great amount of total time spent doing nothing but waiting by a myriad of individuals. During the early stages of implementation to the highway system there would be a combination of both autonomously driven vehicles and human controlled vehicles. This could result to confusion and problems pertaining the reaction of motorists to the driverless vehicles and how efficient the autonomous vehicles can integrate into flow of traffic. The autonomous vehicles would be following all directions of the traffic while human drivers have the choice to go against the law. As period goes on and the autonomous car becomes a more familiar vehicle on the road, traffic would become much less congested. Vehicles could be developed to optimize fuel usage at familiar speeds used on the road. Speed limit could be increased for there is no longer any worry with human error, and the car would know how to operate its situation on the road. With less stop and go traffic, average economy for fuel would also be improved. Vehicles are also travelling after each other consistently which would salvage fuel usage as well. The vehicle would be in position to pick and drop person off, allowing the car to drive several people to different locations. Having small number of vehicles and all of them on the road at a consistent speed would cause far fewer traffic jams. As a result of this, people would be happier with their drive and it would also reduce the amount of time a person must wait on the road. This is presently a problem that departments involved with transportation are working on across the world. With the time wasted on roads being reduced, the ability to improve the overall efficiency of society would be realized. This would also motivate the average person to be timely more than in the past. FOREWORD This literature review strengthens the main report, Auto Ownership and Affect on the Automated World. These publications have been published with the aid of the Federal Highway Administration’s (FHWA’s) Exploratory Advanced Research Program under the technical supervision of the FHWA. This work was done with the aim of providing U.S. transportation research community with a more clear understanding of the present state of research and development and to motivate broader thinking on Auto Ownership based on developments in other countries. This topic has received progressive attention in the developed world, even while interest in such countries like United States has been at a low level relatively in recent years. It is now time that the countries take a fresh look at the institutional and technical issues in line with vehicle automation and its implications for the future of the surface of Auto Ownership particularly when attention in the topic has been widely grown within the automotive and information technology industry. INTRODUCTION Higher levels of Auto ownership results to several problems, such as greater car dependency, congestion and increased carbon emissions. Several factors can motivate households to own more than one car. This paper examines the trends in Auto ownership to affirm what specific factors impact upon these trends. In recent years in many international cities, there has seen increased levels of car ownership. One of the motivating factors of this wide growth in car ownership is the great increase in households with several cars instead of owning one. The research presented in this paper seeks to determine, what factors influence household auto ownership. The following section of the paper gives a review of the literature in this field; which is followed with a description of the methodologies employed in the paper. The paper ends with a summary of the major findings and a number of main conclusions. Hypothesis on Auto Ownership and Autonomous Driving This paper brings forth the most comprehensive discussion to date on the so-called automated, autonomous, self-driving, or driverless vehicles. The research presented in this paper seeks to determine, what factors influence household auto ownership and autonomous driving. What are the implications of auto ownership and autonomous driving to the society, economy and to the environment? Literature review Auto Ownership Models and Autonomous Driving Models to predict variations in the level of Auto ownership and autonomous driving have been under construction since the 1930s (e.g. Wolff, 1938; Rudd, 1951; Tanner, 1958). They are important to the transport planning programs and are of interest to government, environmental protection groups vehicle manufactures, public transport authorities, and public transport operators. De Jong published a detailed review of car ownership and autonomous driving models in 2004. The models found in the literature have been categorized into nine types in this paper: (1) aggregate car market (2) aggregate cohort models, (3) aggregate time series models (4) Heuristic simulation method, (5) models static disaggregate car type choice models (autonomous driving), (6) indirect utility auto ownership and use models (joint discrete-continuous models), (7) static disaggregate auto ownership models, (8) (Pseudo)-panel methods, and (9) Dynamic car transaction models with vehicle model conditional on transaction (autonomous driving). Aggregate time series models always have a sigmoid-shape function for the development of car ownership over time as a function of gross domestic product (GDP) or income. The function grows gradually in the beginning and afterwards rises steeply, ending up approaching a saturation level. For instance the works done by Tanner (e.g. Tanner, 1983), Button et al. (1993), Ingram and Liu (1998), the National Road Traffic Forecasts (NRTF) in the UK (Whelan et al., 2000, Whelan, 2001),Dargay and Gately (1999), etc. These models for autonomous driving have the least data requirements and are appealing for application to developing countries. The demographic power behind auto ownership growth can be expected to remain significant in Western Europe for a couple of decades. Aggregate car market models examples include Mogridge (1983), the Cramer car ownership model (Cramer and Vos., 1985), Manski (1983), Berry et al. (1995), the TREMOVE model (KU Leuven and Standard & Poor’s DRI, 1999), the Altrans model (Kveiborg, 1999), and the software package Thesis (Hensher and Ton, 2002) on autonomous driving. The FACTS model (NEI, 1989; AVG, 1999) and the UMOT model of Zahavi (1979) both are of the heuristic simulation method. The models use the assumption of stability as the beginning point of household money budget for transport over time. The FACTS model differentiates 18 classifications of passenger cars. For every household, yearly income and annual car kilometer covered are drawn at random from household-type-specific distributions, and the budget income share drawn is determined for every category of passenger cars. The household then decides the categories of cars which the costs are nearer to the budget. Static disaggregate car ownership models contain varying choice models that deal with the number of cars possessed by a household. For instance the work by Gunn et al (1978/1979), the Dutch national model system (LMS) for transport (Hague Consulting Group, 1989), Bhat and Pulugurta (1998), the car ownership model for Sydney (Hague Consulting Group, 2000), the disaggregate model within the NRTF (Whelan, 2001) and Rich and Nielen (2001). Joint discrete-continuous models explain household car ownership and car use in an integrated micro-economic framework. The models developed by Train (1986) for California, by Hensher et al. (1992) for Sydney and by De Jong (1989a, b and 1991) for The Netherlands fall in this class .Static disaggregate car type choice models include discrete choice models that deal with the choice of car model for the household given car ownership. There exists lots of publications on static and (pseudo)-dynamic vehicle type choice models ,i.e. the autonomous driving, like Berkovec (1985), Chandrasekharan et al. (1991), Mannering and Winston (1985),Manski and Sherman (1980) and Train (1986). METHODOLOGY Data Sources The data for this analysis resulted from the 1991 and 2001 household origin and destination (OD) surveys which were carried out for the national transportation authorities. The surveys gained from continuous implementation approach, team, authority and also benefited from the long tradition of transportation engineering prowess in Chile MODEL FORMULATION In this research two multinomial logic regression models were tested in this research. The choice variables tested in each of the models were the number of cars for every household. Three levels of car ownership examine where ‘one car possession’, ‘two cars possession’ and ‘three or more cars possession’. Within that model the referent variable is ‘one car available’. The first model examined the impact of several household and personal features such as household composition, age and occupation on multiple car ownership rates. Occupation is employed in the analysis as a proxy for income as the POWCAR dataset does not consist of data pertaining income. The second model tested the impact the mode of transport employed and the proximity to other means of transport has upon multiple car ownership rates. A multinomial logistic regression analysis was used to analyze the relationship between these factors and the number of cars per household. Results and analysis This part of the paper presents a number of descriptive statistics and the outcome from the multinomial logistic regression modeling. These outcomes are then used to identify an area with high levels of car owners Factors that affect auto ownership The results for the age features demonstrated that younger individuals were depicted to be in households with multiple cars. The statistics would seem to make great sense since some of these individuals may be living with their parents who most likely also possesses more than one vehicle. The second group of characteristics detailed the number of resident workers who worked in every household. As expected who households with greater numbers of resident workers were found to have more than one car. The composition of the households was also examined, as an individual would assume that this variable would have an effect upon the number of cars in every household. The results indicated that couples who had children were more likely to have more than one car. Respondents’ occupation was also examined to determine what effect an individual’s profession has upon the decision to own more than one car. The results indicated that professionals such as employers and managers had more likelihood of coming from households with more than one car. The variable that represented the mode of transport used to travel to work showed that households with two or more cars were found to have a higher proportion of individuals driving themselves to work places. The last three variables tested in this study relate to the locations in where the individual lives. Geographical areas known as Dedicated Electoral Districts (DED) are the smallest size areas where the Census data tested in this study are enumerated. Availability of public transport variables examine if the respondent lives in a DED which has a rail station and also the number of bus stops in the DED. These variables are tested to confirm if public transport availability affects a household’s choice to own more than one car. The last variable tested measures the effects that residential density has upon the decision to have more than one car. Residential density variables varied from less than 1,000 individuals per square kilometer to more than 12,000 individuals per square kilometer. The results indicate that those individuals residing low populated areas were more likely to posse’s more than one car. Table showing how Auto ownership is impacted by various variables Results of the Multinomial variables The set of variables related to household structure and tests how household composition affects upon the number of cars available (from the table above). The findings depicted that single persons and single parents are more likely not to posses several cars. Couples, together with couples with children were found to be most likely to have multiple cars in their possession. Couples with resident children older than 19 where shown to be most likely to have three or more cars available. Presumably this great probability is because the resident children may have bought a car. As an individual would expect couples with no children were shown to be unlikely to own three or more cars. The individuals’ with occupation were found to have an effect on probability of having more than one car. The results indicate that semi skilled or unskilled laborers’ and agricultural employees were unlikely to emanate from a household having more than one car. The results also depict that employers and managers, great professionals and farmers were the individuals more likely originate from a household with several vehicles. CONCLUSIONS Data Summary Table 1 below presents the variables employed in the models and together with their descriptive statistics. The data shows the increase in household incomes and the great concentration of households in the average income groups. Consistent with this, the share of zero-car households went down by 5%; the greatest increase was in one-car households. Labor involvement increased modestly as 0 worker and 1-worker households declined (consistent with increased women in the workforce), as 2 and 3 household workers increased. The number of children per household slightly decreased, with many households having one child in 2001 and a smaller having three children TABLE 1 Variables, Definitions, and Descriptive Statistics Variable Description 1991 2001 Mean or proportion Mean or proportion #Autos Autos per Household 0.48 0.54 Household has 0 vehicles 63.10% 58.56% Household has 1 vehicles 28.20% 31.40% Household has 2 vehicles 6.90% 7.86% Household has 3+ vehicles 1.80% 2.18% Household Monthly Income Category (1991 Pesos) 1 (0 to 41,000) 23.67% 6.76% 2 ($41,001 to 72,500) 25.18% 12.09% 3 (72,501 to 110,400) 16.94% 15.28% 4 (110,401 to 172,500) 10.98% 21.46% 5 (172,501 to 262,000) 6.50% 17.83% 6 (262,001 to 405,000) 5.58% 12.19% 7 (405,000 to 1,000,000) 8 >1000000 3.90% 11.30% # Work No. Workers 1.37 1.52 0 Wkr HH 14.60% 11.10% 1 Wkr HH 46.50% 40.54% 2 Wkr HH 26.30% 33.48% 3+ Wkr HH 12.60% 14.87% # Child No. Children 1.61 1.5 0 Child HH 23.10% 24.23% 1 Child HH 23.10% 27.12% 2 Child HH 29.80% 28.84% 3 Child HH 17.60% 13.84% 4+ Child HH 6.40% 5.97% Dist to CBD OD Zone centroid meters to Plaza de Armas 9248.7 10044.5 OD Zone Centroid w/in .5km Metro 13.00% 13.74% DI Diversity Index 0.24 0.20 Res Dens DU per hectare 28.9 33.95 CONCLUSIONS The outcome of this paper shows that a number of factors have effects upon a household’s choice to own multiple cars. The outcomes exhibited for example in Dublin almost half the number of households has two or more vehicles and this raised up to 66% in the study locations. These great figures detail the level of the problem in Dublin. The outcomes of the analysis presented in this paper show that several factors affect the number of cars owned. As per anybody expectation factors like the number of residential workers, age of the individual and the composition of the household all impact upon the number of cars possessed. The occupation of the respondent didn’t have great impact as one would have imagined. The results exhibited it was not only the individuals in highly paid professions that were disposed to several car ownership. Public transport availability options were also found to impact upon car possession. In the study localities despite 54% of the population having a rail station access, auto ownership rates were still found to be high. This outcome may be due to the fact that the service of offered does not service areas required by the residence or other factors like composition household being more important. The research given in this paper offers an example of what factors affect multiple car ownership. The outcome presented in this paper could also be employed as a stepping stone in the process of identifying neighborhoods’ which would be best suited as localities for pilot schemes to promote car sharing or electronic vehicles, or rather any other policy to control car ownership levels. REFERENCES 1. Axsen, J. Kurani, K. Early U.S. Market for plug-in hybrid electric vehicles: Anticipating consumer recharge potential and design priorities. In Transportation Research Record: Journal of the Transportation Research Board, No. 2139, Transportation Research Board of the National Academies, Washington D.C. 2009. pp 64-72 2. Central Statistics Office. Census Reports 1991 – Volume 11, 3. Dargay, J.M. Determinants of car ownership in rural and urban areas: a pseudo-panel. Transportation Research Part E. Vol 38. 2002. pp351-366 4. Dissanayke, D. Morkawa, T. Investigating household vehicle ownership, mode choice and trip sharing decisions using a combined revealed/stated preference Nested Logit Model: case study in Bangkok Metropolitan Region. Journal of Transport Geography. Vol 18. 2010. pp 402-410 5. Doucette, R.T., McCulloch, M.D. Modelling the prospects of plug-in hybrid electric vehicles to reduce CO2 emissions. Applied Energy, 88, 2011, 2315-2323. 6. Duke, M., Andrews, D., Anderson, T. The feasibility of long-range battery electric cars in New Zealand. Energy Policy, 37, 2009, 3455-3462. Read More