Travel behaviour implications of Automated or Driverless cars in Australia
This paper aims to research Travel behaviour implications of Automated cars in Australia, based on existing research in this field and by analysing the information published by academic authors. This paper gives an overview of Factors affecting travel behaviour, brief discussion about Travel behaviour changes and voluntary travel behaviour change, adapting automated cars effects and changes in travel behaviour and the discussion about Australian policy/ law changes/ changes are expected in future. Also this paper states the literature review and design of research investigation.
This project report provides the introduction of Travel behaviour implications of Automated or Driverless cars in Australia; that is being implemented for semester 1 2017 in completion of the subject Project 1 for Bachelor of Engineering (Civil) at Queensland University of Technology(QUT). This proposal will be outlining the Literature review, objective of the project, research methodology, discussion, conclusion and recommendation.
This project is about investigating the travel behaviour of people and what are the factors influencing travel behaviour or changes in travel behaviour. In addition to, will they adopt new driving technology/ what are the changes expected to in future travel behaviour.
According to University of Oxford Transport Studies Unit (2000) Travel behaviour can be defined as “It can generally be referred to as the study of what people do over space and how people use transportation”. Mode of transportation is being changed over the years and it is expected to change in future as well. Tesla (2016) states that Automated vehicles (AVs) or driverless cars are predicted to be ready for public by 2020. AVs can be used for public transport or Driver less cars can be used by individuals; when these new technology Vehicles hit the market it may might make changes in travel behaviour of people as well. So, it’s essential to research and analyse the travel behaviour/travel behaviour change. This project will also outline the impacts of AVs and changes in laws/policies and it will describe the solution and work break down planning to meet all the objectives for this project.
|Queensland University of Technology
AIM & OBJECTIVES
There are several objectives needs to be considered to fulfil the demand of this project. They are listed below:
- Determine the Travel behaviour factors
- Finding required changes Travel behaviour
- Define the effects of adapting new technology towards travel behaviour
- Outlining the law/policy changes made/required changes
- Implications of AVs connected with Travel behaviour
- Validate the findings further through certified references
To achieve the listed objectives firstly vital factors of travel behaviour and travel behaviour change were gathered. Within the gathered information relevant data for this project will be sorted and organised in structure. Secondly AVs data collected to compare the travel behaviour of people using AVs and Present/Past travel behaviour. Also Laws/policies regards to safety of Transportation/ factors gathered before will be discussed. By stating the finding of this research conclusion/recommendation will be outlined. Required journals, books, article and other academic data were obtained from QUT Library data base and Google scholar. Also Statistics Data gathered from to Bureau of transport statistics, Department of transport and other government departments.
Literature and background
Factors affecting travel behaviour
Past research regarding travel behaviour varied but can be divided into two broad groups; socio demographic and life style factors may influence travel behaviour. (Ahmed, 2015). Most essential socio demographic factors which influencing travel behaviour including age, house hold composition, income, gender and car ownership.
Urban form and Travel Behaviour
There is a complicated connection which explains land use and design proposals will influence the cost of travel and thus the type of trip is undertaken; its shows that urbane form can impact travel behaviour. Study by Bonsall (2009) shows there is relationship between land use and travel behaviour statistically insignificant. Also socio demographic factors were significant statistically. Built environments three core influencing variable are density, diversity and design; density and mixture of land use was a significant effect in choosing travel behaviour mainly in whether to use share car, drive alone or public transport. High density of mixed land use workplace will produce high level of public transport.
Bonsall (2009) randomly selected households in suburban Adelaide to decide if urban form influenced travel behaviours. It is important to understand that pedestrian oriented locations with high density mixed land use and good quality urban design might decrease car usage and might increase the sustainable transport mode
The authors resolved that this was the case although warning of the limited range of suburbs used in the study. Participants in the study were located in only four suburbs of uniform size and density. Thus, the authors warn that it might be misleading to suppose that the results would be applicable to the wider metropolitan areas of Adelaide. The study found that low density, single use, large area zoning commonly found in conventional suburbs limited the ability of participants to walk or cycle for their daily travel needs. Proximity to local shopping and service centres and local networks encouraged a wider choice of sustainable travel modes. the location of suburban development away from major activity centres encouraged the use of the private car and reduced the use of other travel modes.
suburban location and the distance from the city on travel behaviour and found a number of major relationships. The closer the participants lived in city, then they likely to walk or use a cycle to get to the facilities located in city.
Socio-demographic Factor and travel behaviour
Effect of socio demographic factor on travel behaviour and found some important relationship between travel behaviour and factors such as age, gender, household composition and income. Number of Daily trips changes depend on employment and health status.
Changes in travel behaviour due to gender was an important factor, in some places with women recognised as being more likely to adopt sustainable travel behaviours compared with men. Women were willing to minimise using cars than men, further certainly to reduce environmental impacts of travel mode. Also Brog & Erl (2009) conclude that gender must consider as a factor in attitudinal research.
Household composition and income were also discovered as an influential on travel behaviour. Research by Cairns (2008) found that households with children have different travel behaviour. These households are mainly depending on car as their primary travel mode and households consists of students and non-workers are mostly using non-motorised form of transport. Main outcomes were that households on better income were usually own and use car and families with small children were mostly using car than single families. The impact of residential environment, expressed by density, diversity and provision of public transport services, on modal choice was also high.
A collection of psycho social factors has been identified as influencing travel behaviour particularly in car owning younger males.it was recognised that psycho factors inclined to use individual and qualitative measure and discoursed the following as per Castro (2009)
- Feelings of power
- Feelings of achieving ‘street cred’
- Protection from socially undesirable groups
- Feelings of prestige within peer group
- Identification with selected peer group
- Feelings of greater autonomy
- Perceptions of greater skill and competence through car ownership no mention of greater femininity amongst female car-owners and users
- Non car-owners/users deemed to be ‘eccentric’ and hence undesirable
Economic theory is another perspective that has featured heavily in research on travel behaviour change. It has been argued that travel behaviour decisions are the result of individual evaluation of the costs and benefits of various travel options. Using this logic, effective behaviour change initiatives need to increase awareness of the benefits of sustainable travel choices, and reduce or mitigate the costs of choosing these travel options (Papagiannakis, Baraklianos & Spyridonidou,2016) As with other investment decisions, the true cost of transport choices is not always apparent (for example, the total cost of a trip involving a private motor vehicle includes maintenance and depreciation, as well as fuel costs) and there is benefit in increasing awareness of such elements. In challenging existing travel decisions, it is necessary to increase the clarity and linkages between transport choices and social, economic and environmental implications. A potential weakness in this approach is the focus on rational decision making, where non rational factors such as habitual behaviour and personal identity related choice are not considered. However, economic theory is useful in its simplicity as a broad guide to achieving behaviour change.
Research has shown that the effectiveness of transport information is highly context- specific. Demographic characteristics, travel patterns and technology factors, all contribute to the effect that information has on travel behaviour (University of Oxford, Transport Studies Unit, 2000).
TRAVEL BEHAVIOUR CHANGE
Travel behaviour change can contribute to improving the efficient use of transport infrastructure (Cairns, 2008) It is an attractive policy instrument for addressing urban congestion, as the focus is on incentives towards sustainable travel behaviour, thus avoiding the political challenges such as road user charging, which use disincentives to achieve behaviour change (Monash University, 2016)
In the South East Queensland context, travel behaviour change initiatives are strongly supported, and are seen as having particular importance in managing the increasing demand for services that has accompanied rapid population growth. The emerging Queensland Government transport policy document, Connecting SEQ 2031 (2011), recognises the key roles of both incentives and disincentives in changing travel behaviour, with the Travel Smart suite of programs as the primary delivery vehicle for achieving voluntary travel behaviour change.
To define the role that transport information can play in contributing to travel behaviour change, it is important to first understand the conditions that surround the process of such change. There are effectively three steps involved in voluntary behaviour change: the decision to change, actual behaviour change, and the continuation of changed behaviour (Department of Transport and Main Roads, 2011) To achieve a desirable transport outcome such as reduced single occupant vehicle use, attention to all three steps is necessary. One of the most important factors within the change process is motivation.
If information is to be an effective agent for travel behaviour change, it must play a role in motivating transport users to change. However, there are significant limitations on the effectiveness of information. Conventional wisdom suggests that if transport users are presented with information on travel times and costs, they will choose the travel option that best satisfies their journey and personal requirements. Yet this approach fails to account for the complexity of human behaviour (Department of Transport and Main Roads,2011).
Research has shown that travel behaviour decisions are the result of a complex interplay of factors, many of which are not linked to rational evaluation of trip characteristics such as time and cost (Witlox, 2007). It is suggested that if transport information is to realise its potential as a change agent, it will need to be embedded in a broader change strategy that uses persuasion principles to influence behaviour (Bonsall,2009).
Theory of planned behaviour
According to Curtin University of Technology (2016) theory of planned behaviour elaborates in terms of individual beliefs. This can be including behavioural beliefs, normative beliefs and control beliefs, which correspondingly effect attitudes, subjective standards and perceived behavioural control. Probability of possible are results of behaviour which beliefs behavioural beliefs. Defining individual attitude to behaviour is the result appraised by individual. Department of Infrastructure and Regional Development (2012) says Individual behaviour might be depending in the approval of referent individual; this is signified as normative beliefs. Subjective norms are about a behaviour observed by the normative beliefs and the individual motivation to share car with referent individuals. Control beliefs are beliefs about probability that factors will simplify or limit a behaviour. Perceived behavioural control is the outcome of those control beliefs gathered into account the perceived power of each factor simplifying or limiting a behaviour. Significant individual beliefs were pressured by the theory of planned behaviour. External factors are not taken into account including urban form. But urban form characterises can be considered as simplifying or limiting behaviour factor. Individual perceptions of urban from included in control belief. Furthermore, subjective views of these factors are vital than objective measurement of it, also attitudes and social norms are part of theory.
Evaluation of behaviour-change interventions
Indicators of behaviour change
According to Department of Infrastructure and Regional Development (2014) challenge is determine the properties of marketing intervention, people’s actions were influenced by some uncontrolled factors. Evaluation methods are available and each has advantages and disadvantages. When analysing the travel behaviour change project, it is recommended to practice multiple of evaluation methods. If analysis of multiple method results in the point of view and demonstrate more or less, same magnitude of change can be reason for well confident in results. In travel behaviour change involvement there are three main measure to evaluate including marketing indicators, external indicators and behavioural indicators.
Marketing indicator contains number and type of information request and good feedback from residents. For example, when preferred change is to increase PT use but research may consider the number of stop specific bus timetables requested by households. Measure of success in the traditional direct marketing is analysing the indicators. Dependence on information request might be bold but they are reliable, detailed and easy to understand the indicators. Continuing behaviour change is expected to base on few change in attitude.
As per Department of Infrastructure and Regional Development (2014) TravelSmart assessment have provided participants comments which says attitude change does certainly occur as result of involvement.
External indicators include measured public transport investment. According to
EBSCO (2011) In western Australia, Travel Smart programme able to introduce Smart card ticketing system by gathering bus boarding data collection. “Robust corroborative data” monitoring is specially separates the effect of VTBC (voluntary travel behaviour change) from other influences.
By Measuring the changes in mobility patterns of the residents can evaluate the effectiveness of the travel behaviour change; intervention travel survey can be conducted to analyse this process. From the Survey by Department of Transport and Main Roads (2011) shows the change from single car driver trip to environmental friendly travel modes and it was found by observing the factors mode share, activities and travel time. Elbanhawi (2015) says “As with external indicators, it is important to measure changes in behavioural indicators against a control group to account for background (uncontrolled) factors”. While evaluating the VTBC, majority of importance given to behavioural indicators than marketing and external indicators.
Challenges in the evaluation of VTBC- voluntary travel behaviour change
Changing people’s travel behaviour on a large scale and at reasonable price is an absolute challenge. But in the perspective of global climate change, high fuel cost, and health problem related with inactive lifestyle are the key points to achieve continuous travel behaviour change. VTBC programmes should output positive to the people and also important to aware people of positive outcome.
Automated vehicles concept
AVs concept is similar to many robotic systems which contains three phase design including sense, plan, and act. Making sense of complicated and dynamic driving environment is a vital challenge for AVs. To obtain raw data and details form the environment AVs are fitted with range of sensors, cameras, radars, etc. these obtain data might helpful to the operating software to recommend/ implement action including acceleration, land changing and overtaking vehicles. Overcome these challenges normally use of radar, mono/ stereo camera and Lidar but in AVs combination of surveillance technology is installed.
Levels of automation
It is crucial to understand that automation of vehicle vary from zero to full automation, which can be divided into five groups as following
- Level 0 – No automation, all times drive has to complete and command also control vehicle in steering, braking and other situations regulated by law
- Level 1- specific automation, electronic stability control/ automated brakes are automated
- Level 2 – Combined automation, minimum two control functions are combined to work automated such as cruise control with lane centring.
- Level 3- Limited self-driving automation, under particular traffic conditions or environmental conditions driver surrenders full control to the vehicle. But if there any changes in traffic/ environmental condition driver has to take control of vehicle.
- Level 4 – Full self-driving automation, Vehicle is designed to observe road/environmental condition and designed to function according to the change by satisfying the entire trip with safety.
Advantages and disadvantages of AVs
Although transportation is a success of societies, however there are negative impacts of transportation as well such as pollution, accidents and human errors. Huge number of researches concludes that major transportation effect are caused by human driven vehicles (Fagnant & Kockelman, 2015). Cost of transportation differs from direct cost of travelling such as transport tickets, petrol cost, vehicle registration, vehicle maintenance, and licensing. Indirect cost is hidden on society which are traffic congestion, environmental pollution, security/safety, and accidents. AV technology have the potential noticeably decrease the impacts which is existing. It was found that external cost is forced on society including low income individuals who is relay on public transport. AVs can also can produce more advantages including improving land use, mobility and improving accessibility. There might be some disadvantages as well but generally believed that advantages are higher and most important than disadvantages.
Travel pattern and travel behaviour influenced by urban form. It was found that low density, single land zoning produces longer travel journey and produce more reliance on car as main use of transport. In high density land use with more chances to choose sustainable transport and with mixed zoning expected to encourage sustainable travel behaviour.
Travel pattern and travel behaviour also influence by socio demographic factors as stated before. Choice of travel mode and length/duration of journey are depending on socio demographic factors including household composition, gender, age, income and car ownership; but important travel behaviour influencing factors are gender and household composition. Moreover, in determining the peoples travel choice, numerous of psycho social factors are influencing. Perception of safety, masculinity and power are major causes of travel behaviour which are part of psycho social factors. There is a view that car can increase social standing in society and may increase the respect in local community. It is hard to summarise on relative impact of urban form factors, socio demographic and psycho social factors. There is an extensive difference in choices of factors measured in influencing travel behaviour.
potential impacts of autonomous vehicles
- AV processes are fundamentally changed from human driven vehicles.
- AVs can be programmed to not break traffic laws.
- They do not drink and drive.
- Reaction time is quicker than human
- Enhance to smooth traffic flow
- Better fuel economy
- Lessen emission
- Able to deliver freight
- Able to move travellers without licence
Travel behaviour impacts
Congestion reducing impacts and safety of AVs have possible influence to make important changes in travel behaviour. For example, AVs might be able to transport disable people, elder people and young people which might create motorway capacity demand. AVs may change parking patterns because it might be able to park in less expensive areas also as AVs can assist multiple persons; car and ride programme may expand. These ideas might expand automobile oriented development and vehicle miles travelled (VMT); but VMT might bring concerns connected to high automobile use including high emissions, more oil dependence and high obesity rates.
All the drivers testing AVs on public roads are instructed to be licensed and prepared to take over vehicle operation in many part is U.S including Washington, Florida, Nevada, California and Michigan. If AVs able to satisfy the safety drive requirement could be relaxed and automated vehicles might be permitted to transport children and humans. According to REFFOR@#$%^& while some people stop driving because of the physical limitation, but other drivers try to overcome these physical limitations including neglecting traffic congestion, poor weather, self-regulation and night time driving. Automated vehicles should ease personal independence and mobility at the mean time safety also need to be increased which could increase the demand of automated vehicle.
Cities with high movement of people especially elderly people, also lower traffic congestion and smooth traffic flow, in this case; most of the cities will expect increase in VMT connected with emission, congestion and crash rates expect in the situation of demand management strategies are considerately implemented. But Automated vehicles advantages might be more than the negative impacts of VMT. For example, if VMT about to double, there will be a decrease in crash rate per mile travelled by 90% which produces a fall in the overall number of crashes and related traffic delays and injuries by 80%. Public infrastructure like communications system with traffic signals are planned to support these capabilities. Though adverse effects including health effects, emission and sprawl; might not be enthusiastically lessened.
It is potential that already congested roads and other motorway infrastructure might be affected as well because of increased number of trips. REFFOR@!#$%^ argues that there is possibility that highways might be able carry more vehicles but in the peak period traffic congestion or traffic delay may not be decrease noticeably. REFFOR@!#$%^ says that total emission per day may not decrease although emissions per vehicle mile travelled decreased. Automated vehicles could respond to increased demand by using its AVs features including quicker reaction time, efficient spacing between vehicles and smarter routing in coordination with intelligent infrastructure. Arterial road increase or decrease in congestion mainly rely on induced VMT, benefits of AVs, and use of demand management strategies including cost of roads. Because of the smooth travel emission expected to decrease, REFFOR@#$%^ says its is estimated that acceleration and deceleration expected to decrease by 20 % and it may lead to decrease in consumption of fuel and associated emissions; therefore, AVs might increase VMT but emissions per mile might be decreased.
Further petrol saving might increase by AVs smart parking decisions which could avoid travelling for parking place. For example, AVs able to drop off and pickup then it could communicate with parking infrastructure to settle down. This feature of AVs can expand car sharing and dynamic ride sharing by permitting car rentals on real time rentals per minute or per mile basis for nearby places. If this programme gets successful, this may offer opportunity to expand this programme because users may able to book a vehicle online or using mobile apps and it will act as taxi on demand which will take to the place where passenger wanted to go. Research by REFFOR@#$%^& says that single shared AV can substitute approximately ten private or household owned vehicles, it was concluded by using agent based model for allocating vehicles around city area.
The influence of pricing on travel behaviour
Cost of travel also can determine the travel behaviour. Hensher & King (201) studied the availability of parking spaces and the rate of parking on travel behaviours in Sydney. In that survey participants were required to consider six alternate circumstances for parking in CBD; in park and ride facility or switch to public transport. it was found 97 percentage of the responses the rate of parking option is the most important factor to determine travel mode. stronger policy plan is essential to decrease the need for driving over the provision of public transport infrastructure at a suitable cost.
What types of regulatory challenges are there?
Regulations need to find if all vehicles must be driven by human even if the vehicle offer fully automated technology feature. Also its important to maintain privacy principles, create guidelines to decrease legal liability problems and affordable access to accident data. Government also need reflect on software updates on AVs which is how the road worthy is defined. There are some impacts of vehicles safety as well if anything gone wrong with the software or virus problems in the system may going to affect the safety of the vehicle.
How might road infrastructure change?
According to REFFOR@!#$%^#$% there is a poor regularity in road marking, road signage and other road infrastructure in Australia which is crucial to make nationally consistent law to avoid conflicts in AVs behaviour. It is expected to be in urban environments so it is important to suggest more connected infrastructure which may including lowering of speed limits. In regional environment AVs should be reliant on inbuilt technology, it is also important to accommodate poor road infrastructure including lack of connected infrastructure.it might be recommended for heavy AVs to use separate routes and traffic corridors in short terms because of the difference in infrastructure.
What could that mean for insurance?
Insurance industry need to be changed because of the introduction of fully automated vehicle including personal insurance and especially in liability issue responsibilities. Removing humans as a part of driving is going to decrease dangerous drives from roads because vehicles will observe data from surrounding with sensors and will make decision according to that. There is also possible growth in independent mobility option because before disabled people and old people were excluded but introduction of driverless vehicles causing the rise in mobility option without increasing road safety and insurance risks. Because of the lower accidents rate personal injury insurance premium expected to change and insures need to reflect who has the operational control of the vehicle. National injury insurance scheme should confirm that passengers are secured by same level of personal injury cover nevertheless of how and where incident happens. A national liability framework should have understood by the car manufacturers, insurance companies and other organisation to make easier for people to get proper treatment or compensation.
Passenger comfort is important to analyse because of the long transit time and high expectation of the consumer. Ergonomic factors were researched traditionally including noise and seat vibration. After the introduction of the automated vehicles, succeeding loss of controllability might lead to a shift towards other factors other than ergonomics factors including safe distance keeping, vehicle control and motion sickness.
Traditional Vehicle Ergonomics
Passenger comfort may appear to be an individual term; however, evaluation methods have been studied well in literature. According to REFF@#$%^&* passenger comfort factors are noise, vibration, temperature and air quality.in vehicle design process Nosie, vibration and harshness(NVH) is highlighted. Few factors are accredited to NVH such as road, tires, brake, power train, engine noise and wind. Road and load disturbances are two type of conflicts that passengers are facing. Low frequency/ high magnitude disturbances are caused by drivers control of acceleration, braking, and turning; which is identified as load disturbances. They were induced in the longitudinal and lateral directions with respect to a front facing traveller. Assessment of noise with respect to its effect on the intelligibility of speech which is ISO TR 3352 standard was used to analyse noise depends on interruption of people capability to conduct conversation.
Acoustic metrics are the contributing factors to comfort of the passenger which includes frequency, tonality and sound level. Unwanted sounds may be effect from tires, clutch, engine, exhaust and wind. There is also noise causing from vibrations of seat. Passenger comfort contributing factors are applicable for AVs passengers as well because vehicles are operated by mechanical design than autonomous behaviour. Load disturbances also an important factor which was influenced by performance of the drive; now this will be rely on vehicles behaviour. To recognize the comfort of passenger in AVs complete human test is essential. Passenger comfort influencing factors will be discussed in the section below.
- Resulting Forces
Common tactic in improving the comfort of the passenger is to improve the moment of the vehicle which can reduce the resulting force and jerk acting on the passenger. By using the proper suspension and seat design can reduce the road disturbances which related to subsequent vibration and vertical force. Due to poor steering and acceleration of the vehicle may result in horizontal forces. Forces causing on the passenger in different axes and their causes shown in the figure below.
more importance given to vertical oscillations which is exposed to passenger. Research by REFFOR#$%^& recommend that ISO 2631 1 standard (Mechanical vibration and shock. Evaluation of human exposure to whole-body vibration. General Requirements) under rates the comfort of the passenger which is lateral oscillations. But lateral force cannot be controlled and its driver specific. However, for AVs horizontal force might be influenced. For a given route velocity pro files for nominal longitudinal jerk were applicable. For bounded acceleration, velocity and pre-determined path were solution for time optimal velocity planning approach to the robotic manipulators.
To manage resulting forces and to avoid overshooting suggested solution would be smooth control. This can be accomplished by creating continuous routes to simplify tracking process. Car trajectory method frequently rejects path continuity of Automated cars. Euler spirals were considered for planning with continuous curvature. Complication of their synthesis and real time execution inhibited use for time dangerous applications including navigation in highway. Euler spirals usage is inadequate to modest tasks such as parking assistant system. As REFFOR@#$%^& stated Parametric vector valued curves were proposed for car-like robot planning with continuous curvature and continuous velocity and acceleration. To decrease the overshooting and tracking errors planning method can be simply combined with trajectory tracking procedure. To decrease the disturbance in resulting load; path planning, tracking and trajectory generation were predicted. Proper values for longitudinal jerk and acceleration for passenger comfort and safety have been previously projected. Though it is recommended to further test the automated vehicle.
- Natural Paths
- Natural paths are similar to the paths which are created by human. Eliminating sense of having a robotic operator can have created by the impact of executing familiar manoeuvres, which is a main reason for passengers comfortable. This can arise to personal term. However, many researchers try to familiar with this. Road and railway tracks were produced by using linearly changing curvature which is cornu spiral or Euler spiral, therefore passengers felt as adapted. While changing lanes and cars operated remotely, human driver’s human driver can utilize continuous steering command. According to the human wish respectable road layout plans was suggested. There are some factors such as car, road, visibility and age considered to make human driver control model. In between, Road manoeuvres variations and subject driving attitude were observed by researchers for the research of Mimic human control, machine learning and driver behaviour can be used together. Based on the human speed control logged record, adaptive human matching speed control is working.
3) Motion Sickness
Because of the unbalance situation between human vestibular system and visuals sensory system motion sickness created. According to researchers that vehicle drivers optimise visual references on the path for connection of curvature. As a result, passengers were liable to motion sickness where they didn’t consider static scenes inside the car and didn’t keep the visual references. According to Turner and Griffin state that some passengers inclined to motion sickness but, accepted the importance of having developed brief view of the path. Further researches indicated that lateral acceleration causing of driver’s methods of turning is the major reason for motion sickness of travellers. Furthermore, another research indicates that giving sensory response for travellers can help to eliminate motion sickness.
The change from human control to autonomous driving needed some further studies among motion sickness causing factors from a planning viewpoint. The sensitivity to motion sickness could increase by the less controllability, this can be eliminating by using DVI for travellers’ experiences. The way which is a reason for motion sickness among passengers was minimum frequency sideways movement causing from steering. To increase the speed of the plan sequences, discretise the control space and discontinues steering are normally used. Linear curvature planning algorithm are planned to use eliminate the sudden changes in steering, resultant force and frequent movement, which can be protect passengers from motion sickness. Continuous trajectories and proper steering control in the vehicle can provide better travelling conditions.
DVIs can be optimised to bring feedback of sensation to travellers from the motion sickness treatment diagram which is in the Fig.10 for exemplification. Further variations may be needed, to vehicles’’ internal model to develop passengers think and reduce side frequent movement.
Conclusion and recommendation
During the past 5 years, many active researches have been performed on AVs. Moreover, AVs have been brought to near readiness as a result of the effort of universities and the manufacturers. The transportation cost associated with AVs are believed to be low. As calculated, AVs can save up to $2000 – $400 per annum per AV when travel time reduction, crash savings, parking benefits and fuel efficiency is taken into account (! @#$%^&*). However, AVs are still on the basic stage and with massive improvements, it is possible to achieve higher degree of interest.
AVs have faced several challenges on its way to implementation. These challenges include acceptance of the general public, social impacts, communication, ethical challenges, planning, policies and standards (! @#%$^%&^&*&). System security and integrity has arisen as a main threat for the system software which is needed to be considered. On the other hand, it is important to understand the implemented policies by the drivers to perform their job with relative to the vehicle capabilities and for the safe operation. Another challenge faced is to connect several intelligent vehicles systems with each other which arise an issue related to the processing and analysing of large data sets (! @#%$^%&*&). The research paper is based on both positive, negative and direct, indirect effects of the emerging technology of AVs. The areas considered are include land use, vehicle distance travelled, safety, parking, variation in demand and the fuel consumption. It is also focused on the impact of AVs to result in an efficient and smooth traffic circulation. Finally, the concept of vehicle navigation is used to overcome the problems arise when AVs are integrated with non-AVs.
The new technology of AVs should be introduced and implemented smoothly among the general public. Apparently, the AV technology is advancing even with or without the legislative actions at the federal level. However, these efforts mainly control the progress of AVs in the future. Smart planning, vision and regulatory actions defines the success of the AV technology. Discussed below are three main procedures to address the arising problems.
Expand federal funding for AV research
Car manufacturers have invested a variety of resources on the researches based on AVs. Meanwhile, the understanding about the impact of the AVs on transportation system is lacked among the general public. This research has highlighted the missing key links in the AV research. In order to achieve a massive success in AV research, a strong federal support is essentially needed to fund these innovations.
The understanding of the AV technology becomes crucial when it occupies the market. In order to overcome these gaps, national agencies such as US Department of Transportation (USDOT), The National Science Foundation, local urban/rural planning and transportation agencies and other main stakeholders should actively involve in the course.
Develop Federal Guidelines for AV certification
USDOT should implement transparent regulations to develop a framework and a set of national guidelines for the certification of AVs at national level. Even though National Highway Traffic Safety Administration has already developed a set of principles for AV testing, federal government is responsible for the certification of AVs for the use of general public (! @#%$%^&*&*^%$). Such regulations will lead to have a better AV system with regards to safety and operation. The set of regulations has been published by US Department of Transport in the Manual on Uniform Traffic Control Devices (MUTCD). This approach promotes a single set of rules to use national wide while a few chances might be applied to suit the local government needs. This helps the AV manufactures to maintain the quality of product with relation to one set of national codes instead of matching many potential set of requirements.
While compiling the national set of regulations for the certification of AVs, current local government certification rules can be used as a reference material. Potential regulatory downsides should also be accounted by the policymakers in order to avoid the excessive cautions which may be applied on the AV manufacturers. Moreover, such shortcomings in regulatory aspects can result in lack of motivation in AV manufacturers to perform the researches (! @#$%^&*&^%%).
Determine appropriate standards for liability, security, and data privacy
Liability, security and data privacy plays a key role in the implementation of AV technology. The motivation among the manufacturers for AV development will be considerably rise when the federal government address these concerns. Liability standards will keep the balance between the manufacturer responsibilities and the government requirements without an extra pressure on both departments. In order to prevent cyber-attacks, it is important to robust the cyber security to address the vulnerability of the AV systems. On the other hand, the consumers of AV technology might have potential concerns about the data collection of their personal travels. Therefore, the privacy concerns of the general public should be addressed by the policymakers to prevent the relative potential threats.
It is complex to understand travel decisions influencing factor by observing the role of transport information. Travel behaviour has been studied broadly with precise importance on the factors that influence decisions and preferences for travel choices. To explain the influence of travel information which is affecting travel decisions, it is essential to reflect factors that apply to modal usage patterns and boarder process of behaviour change.
This paper initiated with analysing the travel behaviour change. In south east Queensland improve the efficiency of transport infrastructure voluntary travel change is a supported policy however it relies on motivation provided. It is vital to comprehend that choices of travel are the effect of complex interaction of factors, at the mean time information can influence significant role in motivation. It is recommended to engage traditional persuasive techniques from design information, if passengers are to be influenced in their travel choices providing trip information including cost of travel and duration is essential. public transport and road based methods are supported by a growing body of research that has recognized a number of significant data and user characteristics. Public transport passengers require different information’s deepens on their personal experience in the system. Mostly tourists depend on the basic service information such as structure of fare and modal route coverage. Experienced users are more interested in information related with delay information and real time arrival information.
Ahmed, F., A B M Rocknuzzaman, & Islam, S. F. (2015). Automated vehicle tracking by GPS modern tecnology. International Journal of Computer Science and Information Security, 13(7), 39.
Bureau of transport statistics. (2016). Household Travel Survey. Retrieved April 01, 2017, from http://www.bts.nsw.gov.au/
Bonsall, P. (2009). Do we know whether personal travel planning really works? Transport Policy, 16(6), 306-314. doi:10.1016/j.tranpol.2009.10.002
Brög, W., Erl, E., Ker, I., Ryle, J., & Wall, R. (2009). Evaluation of voluntary travel behaviour change: Experiences from three continents. Transport Policy, 16(6), 281-292. doi:10.1016/j.tranpol.2009.10.003
Byrne, M. (2011). The role of transport information in influencing travel behaviour: A literature review. Road & Transport Research: A Journal of Australian and New Zealand Research and Practice, 20(2), 40-49.
Cairns, S. (09/2008). “Smarter Choices: Assessing the Potential to Achieve Traffic Reduction Using ‘Soft Measures’”. Transport reviews (0144-1647), 28 (5), p. 593.
Castro, C. (2009;2008;). Human factors of visual and cognitive performance in driving. Boca Raton: CRC Press.
Chang, C., Wade, M., Chen, F., & Stoffregen, T. (2013). The effects of driving experience on postural activity and motion sickness in a virtual vehicle. Journal of Sport & Exercise Psychology, 35, S62-S62
Curtin University of Technology. (2016). Travel behaviour a review of recent literature. Retrieved April 01, 2017, from http://urbanet.curtin.edu.au/
Department of Infrastructure and Regional Development. (2014). Transport and Regional Economics. Retrieved April 01, 2017, from https://infrastructure.gov.au/
Department of Infrastructure and Regional Development. (2012). 2012 Statistical Summary. Retrieved April 01, 2017, from https://infrastructure.gov.au/
Department of Transport and Main Roads. (2011). Connecting SEQ 2031. Retrieved April 01, 2017, from https://www.tmr.qld.gov.au/
Dong, X., Yoshida, K., & Stoffregen, T. A. (2011). Control of a virtual vehicle influences postural activity and motion sickness. Journal of Experimental Psychology: Applied, 17(2), 128-138. doi:10.1037/a002409
EBSCO Publishing (Firm), Society for the Advancement of Economic Theory, & SpringerLink (Online service). (1991). Economic theory. Economic Theory,
Elbanhawi, M., Simic, M., & Jazar, R. (2015). In the passenger seat: Investigating ride comfort measures in autonomous cars. PISCATAWAY: IEEE. doi:10.1109/MITS.2015.2405571
Fagnant, D., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part a-Policy and Practice, 77, 167-181. doi:10.1016/j.tra.2015.04.003
Goodall, N. (2014). Ethical decision making during automated vehicle crashes. Transportation Research Record, 2424(2424), 58-65. doi:10.3141/2424-07
Hammadi, S., & Ksouri, M. (2012). Advanced mobility and transport engineering. Hoboken, N.J;London;: ISTE.
Isu, N., Hasegawa, T., Takeuchi, I., & Morimoto, A. (2014). Quantitative analysis of time-course development of motion sickness caused by in-vehicle video watching. Displays, 35(2), 90-97. doi:10.1016/j.displa.2014.01.003
Jeng-Weei Lin, J., Parker, D., Lahav, M., & Furness, T. (2005). Unobtrusive vehicle motion prediction cues reduced simulator sickness during passive travel in a driving simulator. Ergonomics, 48(6), 608-624. doi:10.1080/00140130400029100
Jones, P. M., & University of Oxford. Transport Studies Unit. (2000). Understanding travel behaviour. Aldershot, Hampshire, England: Gower.
Kowald, M., & Axhausen, K. W. (2016). Social networks and travel behaviour. New York;London;: Routledge.
Le Vine, S., Zolfaghari, A., & Polak, J. (2015). Autonomous cars: The tension between occupant experience and intersection capacity. Transportation Research Part C, 52, 1-14. doi:10.1016/j.trc.2015.01.002
Megías, A., Cándido, A., Catena, A., Molinero, S., & Maldonado, A. (2014). The passenger effect: Risky driving is a function of the Driver‐Passenger emotional relationship. Applied Cognitive Psychology, 28(2), 254-258. doi:10.1002/acp.2989
Monash University. (2016). Autonomous Vehicles: potential impacts on travel behaviour and our industry. Retrieved April 01, 2017, from https://www.monash.edu/
Moreira-Matias, L., & Cats, O. (2016). Toward a demand estimation model based on automated vehicle location. Transportation Research Record, 2544(2544), 141-149. doi:10.3141/2544-16
Papagiannakis, A., Baraklianos, I., & Spyridonidou, A. (2016;2017;). Urban travel behaviour and household income in times of economic crisis: Challenges and perspectives for sustainable mobility. Transport Policy, doi:10.1016/j.tranpol.2016.12.006
Petit, J., & Shladover, S. E. (2015;2014;). Potential cyberattacks on automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 16(2), 546-556. doi:10.1109/TITS.2014.2342271
Santurtun, E., & Phillips, C. (2015). The impact of vehicle motion during transport on animal welfare. Research in Veterinary Science, 100, 303-308. doi:10.1016/j.rvsc.2015.03.018
Saeed Asadi Bagloee Madjid Tavana Mohsen Asadi Tracey Oliver. (2016). Autonomous vehicles： challenges, opportunities, and future implications for transportation policies.24(4), 284-303. doi:10.1007/s40534-016-0117-3
Sierpiński, G. (2016;2017;). Intelligent transport systems and travel behaviour : 13th scientific and technical conference “”transport systems. theory and practice 2016″” katowice, poland, september 19-21, 2016 selected papers. Cham: Springer. doi:10.1007/978-3-319-43991-4
Schönfelder, S., & Axhausen, K. W. (2016). Urban rhythms and travel behaviour: Spatial and temporal phenomena of daily travel. New York;London;: Routledge.
Tesla. (2016). Autopilot – Tesla. Retrieved April 01, 2017, from https://www.tesla.com
University of Calabria. (2016). The influence of physical and emotional factors on driving style of car drivers. Retrieved April 01, 2017, from http://www.unical.it/
Tillman, L. (2013). Motion sickness Red Lemonade.
Travel behaviour VOLUME 1 foreword. (2016). Transportation Research Record, (2565), VII-VII.
Turner, M., & Griffin, M. J. (1999). Motion sickness in public road transport: The effect of driver, route and vehicle. Ergonomics, 42(12), 1646-1664. doi:10.1080/001401399184730
Witlox, F. (2007). Evaluating the reliability of reported distance data in urban travel behaviour analysis. Journal of Transport Geography, 15(3), 172-183. doi:10.1016/j.jtrangeo.2006.02.012
Yeum, C. M., Dyke, S. J., Basora Rovira, R. E., Silva, C., & Demo, J. (2016). Acceleration‐Based automated vehicle classification on mobile bridges. Computer‐Aided Civil and Infrastructure Engineering, 31(11), 813-825. doi:10.1111/mice.12212
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