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1 General description of Connected and/or Automated Vehicles

While the term 'Connected Automated (or Autonomous) Vehicle' (CAV) is widely used, it is important to clarify the meaning of this term in the context of this site. CAV is used as a generic term for a vehicle with a certain degree of automation and a certain level of connectivity.

This site uses a number of closely related acronyms:
  • CAV: Connected Automated Vehicle
  • CV: Connected Vehicle
  • AV: Automated Vehicle
  • HAV: Highly Automated Vehicle

A vehicle can either be connected or automated to varying degrees, or a combination of the two. For this site, the term Connected Vehicles and Automated Vehicles are defined as follows:

Connected Vehicles (CVs)
CVs refer to vehicles with the capability to connect and communicate with their surrounding environment including the infrastructure and other vehicles. This means they could provide information to the driver and vehicle about road, traffic, and weather conditions, as well as routing options. Connectivity could enable a wide range of services to the vehicle occupant. CVs are sometimes seen as a component of a Cooperative Intelligent Transport System (C-ITS). It could be argued that the term CV refers to a broader set of applications than C-ITS, including car entertainment. As such, the two terms are not necessarily interchangeable but are closely related. Communication protocols are being developed to ensure interoperability (see https://en.wikipedia.org/wiki/Vehicular_communication_systems).

Automated Vehicles (AVs)
Sometimes known as autonomous, self-driving, driverless or robotic vehicles, AVs are vehicles that use information from on-board sensors and systems to understand their location in relation to their environment and navigate safely through it with little or no human input for some, or all, of the journey.

Connectivity and automation
Combining connectivity and automation may be desirable, as AVs could use information from external sources to help inform decision-making. Communications could be established between vehicles and infrastructure (V2I) and between individual vehicles (V2V). This could, for example, involve instructions being sent from a control centre to a vehicle, possibly with the involvement of a person. For instance, a traffic signal controller could broadcast the status of the signals to reduce reliance on the vehicle interpreting the signals using its on-board cameras. Connectivity could help automated vehicles to see beyond their sensors. This is why connectivity and automation are often discussed together.

Levels of Automation
The 'Levels of Automation' were originally defined and published by the United States (US) Society of Automotive Engineers (SAE) in 2014, and updated in 2016. In September 2016, the US Department of Transportation (DoT) published the Federal Automated Vehicle Policy. In this policy, DoT referred to the latest SAE Levels of Automation, which led to them becoming global definitions. The SAE definitions, along with the US DoT summary, are included below:

SAE levels.jpg

SAE's definition of the 'dynamic driving task' includes the operational (steering, braking, accelerating, monitoring the vehicle and roadway) and tactical (responding to events, determining when to change lanes, turn, use signals, etc.) aspects of the driving task. It does not include the strategic aspects of the driving task, for example determining destinations and routing waypoints. 'Driving mode' is defined as a type of driving scenario with characteristically dynamic driving task requirements, for example, expressway merging, high-speed cruising, low-speed traffic jam and closed-campus operations. The DoT document also makes a useful distinction: "Using the SAE levels, DoT draws a distinction between Levels 0-2 and 3-5 based on whether the human operator or the automated system is primarily responsible for monitoring the driving environment. Throughout this Policy, the term “highly automated vehicle” (HAV) represents SAE Levels 3-5 vehicles with automated systems that are responsible for monitoring the driving environment."

This site uses the same terminology, so vehicles considered to be of Level 3 and above are termed HAVs. Another way of visualising these levels focuses on how they transfer responsibility from the human driver to the software in the machine:
SAE 6 new.png
Source: https://iqglobal.intel.com/iq-content-library/wp-content/uploads/sites/18/2016/08/Levels-of-AD.jpg

AVs will come in many forms. To help define them it is useful to consider use cases. These might include:
  • Privately owned passenger vehicles with Advanced Driver Assistance Systems (ADAS) (SAE Levels 1-2);
  • Highly automated privately owned passenger vehicles (SAE Levels 3-5);
  • Highly automated passenger vehicles available for hire (single use, SAE Levels 3-5 but more likely to be Levels4-5)
  • Highly automated passenger vehicles available for hire as ride sharing (SAE Levels 3-5 but more likely to be Levels4-5)

Mobility as a Service
The last two options are often linked with the idea of Transport or Mobility as a Service (MaaS). MaaS is this site’s preferred terminology. For more information, go to [insert link to MaaS]. For a MaaS operation, it is not essential that the vehicle is an AV. For instance, Uber services could be considered MaaS. However, the use of AVs for MaaS will be particularly disruptive to transport systems as they do not require a driver, which introduces costs savings and some new issues.

There will be also other types of AVs:
  • Highly Automated Heavy Goods Vehicles (SAE Levels 3-5)
  • Highly Automated Public Transport Vehicles (SAE Levels 4-5)
  • Small highly automated delivery vehicles (these could be footway based)

2 CAV Technology

(Main article: CAV Technology)

The technology necessary to deliver HAVs requires a combination of sensors, hardware and intelligent software. But even this is a simplification. It is possible to further separate functions and components required to achieve a safe and effective HAV. The figure below (based on McKinsey's Self-driving car technology report) summarises the components to achieve full automation. It originally identified 10 components needed for CAVs. The below list has been extended to 12 to include connectivity:

  1. Sensors of different types to gather data about the environment
  2. Computer hardware to run the necessary programmes
  3. A real-time operating system to run the algorithms necessary for successful and safe driving
  4. Perception and object analysis to detect and identify objects, obstacles and tracking them in real-time
  5. Location and mapping to localise the vehicle in a particular expected context and as part of a relevant route to destination
  6. Algorithms, most likely based on a combination of rule-based and Artificial Intelligence learning systems that integrate the information from sensors, location, connectivity and decision making (route to destination)
  7. Decision making, selecting a vehicle route and path trajectory
  8. Drive control translating the outputs from the algorithms into signals for the actuators
  9. Actuation, breaking, accelerating and steering
  10. Analytics, monitoring automated systems operation, identifying faults and delivering fail-safe recommendations
  11. Learning, updating maps, improving algorithms and object detection and identification capabilities
  12. Connectivity vehicle to vehicle and vehicle to infrastructure, and its protocols
12AV elements.png

Although there are prototype HAVs on public roads, there are three key challenges to overcome before allowing their free commercial use:

For HAVs there is a need to move away from using the human driver to monitor vehicle performance and respond to incidents or environments that the vehicle cannot handle. The vehicle software needs to be capable of safely handling possible scenarios that the vehicle may encounter.

Test and validation
While technology is ready for testing at a working level in limited situations, validating it may take years because the systems must be exposed to a significant number of uncommon situations. As this is impractical, methods for testing that involve simulation are being used. This is in particular for hybrid approaches that combine simulation with hardware testing and validation.

Software will remain a bottleneck, with critical issues being object detection and analysis, decision-making and fail-safe mechanisms.

3 Costs, Pricing & Market

(Main article: Costs, Pricing & Market)

Current versions of Levels 4 and 5 AVs are experimental and expensive. Some of the more expensive components are the multiple sensors needed to locate the vehicle and, in particular, to gain information about other vehicles, pedestrians and the context in which the AV is placed. The cost of these sensors is steadily falling and will soon cease to be a price barrier for deployment of AVs. There is an expectation that the premium payable for an HAV compared to a similar non-automated car would be below US$20,000 by the time they are offered to the market.

It is interesting to note that most of the hardware needed for HAVs is already available in conventional cars, including the actuators in a modern vehicle. New sensors, however, which are currently expensive, are needed. In addition, computer hardware and a good deal of intelligent software are also required. This software includes functions such as analytics, operating systems, perception, algorithms, decision making, location, drive control, learning and, eventually, connectivity. The key bottlenecks are, therefore, the reduction of sensor costs and the development of the required software. Once the software elements are ready they can be distributed to any number of vehicles at virtually no cost. The premium price of HAVs will then be determined by the cost of sensors and computer hardware, as well as the policy adopted by the manufacturers to recover the investment costs of developing the software. As several manufacturers and software developers are working on HAVs, it is expected that an interesting competitive environment will develop during the next decade.

As market share, HAV share of a fleet and HAV share of traffic is sometimes confused in the literature, the following definitions are designed to clarify what these ideas represent:
  • Market share or market penetration is the proportion of HAVs sold in any one year compared to the total number of vehicles sold that year.
  • The HAV share of a fleet refers to the proportion of active vehicles that are automated (Levels 4/5) in a particular year and country.
  • The HAV share of traffic is the proportion of vehicles that appear on the roads and streets that happen to be HAVs. This share will be different from the share of HAVs in the fleet. It will probably be higher depending, in turn, on the proportion of HAVs used as MaaS.

Current projections for the market potential of HAVs are low and unconfident, as the technology and business models are still in development. In particular, uptake speed will be influenced by the additional costs of HAVs (beyond the equivalent traditional car), which in turn is heavily dependent on the level of automation and the technology used. It has been argued that the speed of adoption may be quite high, as internal combustion engine cars replaced horse-drawn carriages in less than 15 years in many large cities (for example London). However, other studies recognise that some technologies, including the car, take many years to be fully adopted because of cost constraints. In terms of cost, some people are more constrained than others, as demonstrated by the wide range of car prices and quality currently sold in the market.

One study estimates that the regular car owner won't see a financial benefit from using HAVs until the additional costs drop below $10,000 (this ‘breakeven’ point is reached earlier for users with higher incomes). Assuming market forces only, the $10,000 mark-up is likely to be reached 5-7 years from first commercial introduction. However, a recent survey has shown that on average customers would be willing to pay a mark-up of $3,500 for partial autonomy and $4,900 for full autonomy. It could be questioned how realistically customers can estimate the acceptable mark-up, considering both technologies and business models are as yet unsettled. However, an expert Delphi panel estimated a broadly similar mark-up of $5,100 for full autonomy. A somewhat lower mark-up value ($3,000) is used as the standard price for automated driving in J.D. Powers' annual Automotive Emerging Technologies Study, however, this does not qualify the level of autonomy. On the (untested) assumption that mark-ups of HAV functionality follow a similar trend to that of electric vehicles, mark-up of $3,000 may not be reached until 20-22 years after initial introduction. The counter-argument is that electric vehicle adoption has been limited by efficiency and cost, and the charge time of batteries (and the limited range of). These constraints would not apply to HAVs that are likely to use whatever powertrain is available and attractive at the time.

This is one case where uncertainty about pricing policies by manufacturers, policy developments, confidence in the technology and perceived benefits of using HAVs for MaaS in contrast to owning them will affect the speed of adoption. The role of an expert panel (as featured on this site) is to provide a range of views with respect to these uncertainties in order to help prepare for the challenges of new technologies.

4 Claimed Benefits of CAVs

(Main article: Claimed Benefits of CAVs)

There has been a wide range of attractive benefits proposed for the adoption of CAVs, in particular, that they will save many lives.

The potential benefits of CAVs may include:

  • Increasing safety from reduced number and severity of road collisions, primarily through the reduction of human error. This has the potential to make road traffic as safe as aviation or rail travel, and safety benefits will accrue even for low levels of automation.
  • Increasing mobility for users who are currently unable to drive (new users of HAVs), for example, due to physical, mental or legal restrictions or those that cannot afford the considerable costs of car ownership.
  • Increasing road capacity, ranging from approximately 50% to 300% increase (at 100% penetration of HAVs), depending on the nature of the CAVs and the level of connectivity.
  • Reducing, though not fully removing, the perception of travel as ‘lost’ time and therefore reducing the ‘cost’ of travel time.
  • Alleviating parking problems. HAVs could be sent to serve other users rather than park, or could park in areas of low parking demand.
  • Improving public transport services and accessibility, with potential to provide more flexible demand responsive services.
  • Improving air quality from better driving, vehicle platooning and coordination with traffic signal information for connected vehicles.

These benefits will depend, however, on how many CAVs, or just HAVs, are in the traffic stream, their level of automation and connectivity, regulations in place and theproportionthatisownedoravailableasMaaS.
These are discussed in greater detail in Claimed Benefits of CAVs. Low levels of automation will generate safety benefits. High levels of automation are needed for most personal benefits, including allowing the user to participate in other activities while travelling, removing parking problems, and for new mobility users.

5 Potential challenges of CAVs

(Main article: Potential challenges of CAVs)

Almost inevitably, together with benefits from CAVs there will be some problems and perhaps unintended consequences. Seeking to manage them will be a key task for policy makers and planners.
There are potential challenges associated with CAVs, either as barriers to adoption or as negative side effects, or both. These will again depend on the proportion of CAVs in the fleet and traffic, the form of use, and regulations in place. They include:

  • Generation of more traffic (from trip induction), either due to new users or a reduction of the perceived cost of trips as the user can perform other activities at the same time.
  • The net impact that CAVs will have on traffic volume is uncertain and will depend on the balance between improved capacities, performing only legal manoeuvres and induction of new trips.
  • Reduced employment or unemployment for drivers and associated trades, for example, driving instructors, traffic wardens, and jobs in insurance.
  • Increased environmental impact related to increased journeys, although this may be balanced by increased efficiency due to driving style.
  • Liability issues and the need for new insurance and risk models that take into account the liability of the car owner and car manufacturer, as well as the end-user.
  • Possible failures of technology including the risk of hacking and use in crime and terrorism.
  • Challenges facing the introduction of CAVs, including technological, regulatory, social and political.
  • Equity issues as CAVs may be perceived as a benefit only for the privileged few with access to higher income.

6 Ownership models

(Main article: Ownership model)

For CAVs, there are at least three modes of use:

  1. Direct personal ownership as per conventional cars
  2. Rental of HAVs by the day or hour
  3. The hiring of CAVs or Mobility as a Service

The most important distinction will be between owning and using HAVs as MaaS. The mode of use of HAVs will influence a number of impacts. In the most disruptive case, HAVs used for MaaS will have the greatest impact when they can operate without a driver. In this instance, MaaS could be offered at a lower cost than current taxis or Uber-like services. This type of use is likely to be more attractive in large cities first, in particular in places with parking restrictions and where people are more accustomed to travelling by public transport. It will then extend to smaller urban areas. High urban density and significant MaaS use will reduce the time from booking to service. Companies like Uber and Lyft are well placed to replace human-driven vehicles with HAVs as they have the experience and technology to allocate vehicles in advance to areas according to demand. Nevertheless, there is a need to develop the skills to manage, store, clean and maintain large fleets, a task that is currently assigned to drivers.

The choice of owning or using HAVs as MaaS will depend on cultural influences, the relative cost of use, and the perception that the technology is still in early stages. This perception may affect the attractiveness of investing in an expensive HAV.

Cost of ownership and use are likely to be important variables in this choice. Car owners tend to perceive the cost of ownership as mostly a fixed cost, and that fuel consumption and parking are the only variable costs. The cost of using a vehicle as MaaS, on the other hand, must include not just variable but also depreciation of fixed costs. When a driver is required for MaaS the costs must include their salary. The key questions are whether and when individuals will consider the full cost of ownership and use against the entirely variable cost of hiring HAVs as MaaS.

There is a wide range of estimate of the costs of driving today and the likely costs of using HAVs. The Citi Observatory has compiled a set of estimates of the actual costs of using HAVs as a service. While these are estimates, they imply that fleets of highly automated vehicles could be operated in US cities in the next 20 years for approximately half the cost of using a private vehicle.

7 Powertrain

(Main article: Powertrain)

A HAV could be powered by an internal combustion engine (known as an IC HAV using diesel, petrol or another fuel), a pure battery-electric motor (EV HAV) or a hybrid powertrain. The main advantage of EV HAVs will be the environmental benefits they bring, in particular, emissions at the point of use. When it comes to accidents, congestion and empty seats, the impact will be mostly independent of the powertrain.

The choice will significantly depend on the progress in battery technology over the next few years, and specifically in power density, charging time and effective cycles. These improvements will help to remove concerns about the range they can travel to, without having to pause to re-charge. It will also depend on their form of use. It can be argued that those used for MaaS can be better managed to return to charging points when necessary and appropriate to serve users.

8 Policies and Regulations

(Main article: Policies and Regulations)

8.1 Policy issues

With robust policies and regulations in place, the future of AVs has the potential to be disruptive, and looks very promising. The more transformative the technology, however, the easier it is to miss opportunities. Authorities have a role and duty to achieve maximum benefits and efficiency with this new mode. Therefore, policy instruments should be used not only to minimise the adverse effects of AVs, owned or in MaaS fleets, they should also be used to create a focus on identifying and managing opportunities to improve mobility, equity and welfare.

The following table summarises the impacts of CAVs following a paper on the topic[i]. The assumption is that AVs will use the same powertrain as new cars at the time. The table identifies benefits and costs to society, rather than the individual. Some impacts depended on how AVs will be used. For example, owning or hiring them as MaaS may have different consequences for trip induction and employment.


Each of these forms of use is likely to generate different levels of zero occupancy movements. The highest is likely to be the owned AVs serving family and friends. The lowest is likely to result in MaaS services used in high-density urban areas where coordination of vehicle deployment is easier. Several MaaS fleets operating without coordination and competing with each other are likely to be less efficient and generate more empty movements. To promote greater efficiency in these operations, authorities can develop policy tools.

Both hirers and owners still fail to perceive the actual cost of providing the infrastructure in addition to the externalities of congestion and emissions that they induce on the rest of the travelling and local population. The economic case for a good system of Road User Charges to bring these costs to the fore is well accepted by most professionals. The political and social obstacles to implementing such a scheme are also well known.

Parking management is likely to be one of the policies of choice to influence the balance between hiring and owning AVs. Parking restraint and high parking charges would favour hiring. Different tax regimes for privately purchased AVs, if higher than for fleet purchases, would favour MaaS.
High urban density would favour hiring – it would be easier to secure an AV, as there would be more nearby. Well-regulated AVs for hire may be better maintained and more secure than owned ones relying on annual checks. The security of personal information and protection from hacking it is also very important.

Fleets of hired AVs might be expected to renew them more often, disposing them for recycling rather than reselling the AVs after their economic (or environmental) life has expired. A younger fleet would be less polluting.

Hiring AVs available as MaaS can be seen as available to all whereas owning, especially when a premium is required, is perceived as elitist and less equitable. Replacing workers with machines and computers can also be perceived as an ethical issue more taxing than replacing local workers with foreign ones.

8.2 Policy Levers

Testing AVs on public roads is one thing, allowing them for general use is another. Most of the safety benefits of AVs would be achieved with Levels 2 and 3 vehicles, however sanctioning their public use is not straightforward. Levels 4 and 5 require an even greater reworking of legal and policy frameworks. These, in turn, may depend at least partially on whether the AVs are owned or hired as MaaS.

It will be necessary to prepare specifications for the type-approval of vehicles Levels 3 onwards and their technical tests (AVs MOT). These may include the requirement of timely software updates to be tested for accuracy. Presumably, the tests would be more stringent and/or frequent for hired than for ownedAVs and they may be required to have distinctive markings.

The appropriate authorities also have to specify the conditions for the registration of vehicles, licenses required for ‘drivers’ for each level and how traffic regulations will be enforced. For example, who is responsible if an AV exceeds the speed limit because of a faulty map? The accuracy of maps used by AVs to ensure legal driving may have to be officially approved. There may be a requirement for emergency police control of AVs to prevent crime, for example, the remote availability of a way to stop all movements of a car.

This will also require clarification of assumed liability for Levels 4 and 5. The drivers of Level 3 vehicles will need a license and should be ready to take charge. Therefore, they will at least share liability and will require insurance. For Levels 4 and 5, liability is more likely to be retained by suppliers of the vehicles and no insurance may be required of users. There would be minimum training and a simple test to ensure the vehicle is used as intended, especially for Level 4.

It will be necessary to clarify other legal issues, from who can be the sole or responsible occupant to the level of training, and perhaps licensing, required. The most complex cases would be with Level 3 where responsibility may shift from vehicle to driver at the vehicle’s request. Determination what qualifies as the driver’s or manufacturer’s negligence will be something determined by the courts or legislation.

These measures will help define when each level will be allowed on public roads. But for Level 4 it will be necessary to also define the specific driving domains. This will depend partly on the maturity of the technology. It may be very restrictive, for example only University Campuses, or very general, such as all paved roads with at least two lanes. It would then be difficult to prohibit Level 4 circulation in specific areas, for example, those served by public transport. However, it may be necessary to specify minimum weather conditions, say over 5oC with winds below 30 km/hr and no snow on the ground. Although most of the safety benefits and some capacity improvements are achievable with Level 3, there will be strong supplier and political pressure to speed the approval of higher levels to achieve other benefits and not be left behind. The problem will be that the most significant individual benefits are achieved with Level 5 at a small incremental cost: removing parking problems, allowing new users (previously unable to drive), fully performing other activities while travelling and eliminating liability and insurance issues. These are important individual benefits that will speed up Level 5 adoption that also bring, at the same time, some of the unintended consequences that the policies should seek to manage.

8.3 Transport Demand Management

Once Levels 4 and 5 are allowed on the road there will be a need for improved demand management policies. It is unlikely that AV only lanes would be provided and the effort will be focused on enhancing the benefits and discouraging use that produces disbenefits. In this context, it is expected that the policies will favour for-hire AVs over owned ones and monitor their impact to ensure the right balance is struck. On the other hand, if unemployment is perceived as a key and major problem then the opposite preference will be adopted.

Pricing instruments will be the most flexible, and effective, policy tools to manage AV demand and to provide an alternative source of revenue once fines and parking yields are reduced. In its simplest form, this may involve a lower annual road tax for the preferred form of use. It will be possible to devise parking policies specifically for Levels 4 and 5, treating owned and hired vehicles differently, as is currently the case of some taxi ranks and car club places.

Empty HAVs circulating on congested roads will be a sore sight creating universal irritation and rejection. It will be relatively easy to justify a Road User Charge (RUC) as a measure to manage their use. The charge is likely to be set to reflect the externalities, congestion and emissions, of empty movements. Charge collection will be simplified by the extensive instrumentation of such vehicles including accurate location history. This charge is likely to be acceptable to all, recognising the clear nature of the externality of an empty vehicle in circulation. It will then be a small step to charge the externalities of AVs with passengers and also of other, non-autonomous, vehicles. Any RUC will have the additional benefit of allowing tailored pricing to protect and encourage the use of more efficient forms of transport and discourage urban sprawl.

Unregulated and uncoordinated competition between MaaS operations will not achieve all the benefits available with this new technology. The advent of AVs will also create new opportunities for transport and urban planning and traffic management. AVs will generate considerable amounts of data and some of that can be transmitted wirelessly and in real-time. It will be important to ensure good coordination and availability of these data sources to support better intelligent traffic management and create a more robust base for transport modelling and planning. It would be a great loss if these data sources were allowed to become proprietary and unavailable.

Automated buses, mini-buses, trucks and white vans will also need specific policies to satisfy similar requirements to those of car AVs. As such, they will affect an important group of stakeholders: professional drivers and their assistants.

The adoption of AV technologies and the policies associated with them will have major impacts on groups of people that are often ignored in the policy development process: those unable to drive, professional drivers and associated trades, insurance workers, parking assistants and inspectors.

It will be necessary to design complementary measures to address the equity impacts of AVs. In some countries, the process of replacing drivers with AVs will be slower because of the different costs of capital and labour. But in all countries there will be a need to devise effective means of up-skilling those displaced.

9 Intermodal integration

(Main article: Intermodal integration)

AVs are only a small part of the overall transport system, which comprises multiple modes including trains, buses and planes, as well as a mix of public and private providers. The latter include individual owners of vehicles. The impact of CAVs on other modes will be a complex interplay of competition and enabling influences. For example, while privately operated automated taxis would compete with public transport provision in urban areas, they may also provide a much-needed extension of public transport trunk lines into rural areas. The success of public transport provision and, by extension, of local economies and communities is highly dependent on finding an appropriate niche for CAVs and CAV operators.

10 Infrastructure

(Main article: Infrastructure for CAVs)

Successful deployment of AVs will require significant change to investment in supporting infrastructure. The details of infrastructure requirements are not yet clear: they are inextricably linked to choices made during development of AV technology. For example, cameras require machine-readable and well-maintained road signs and markings. Whether investment in roadsigns is required depends on how heavily AVs will rely on camera systems for perception. Nevertheless, it is clear that supporting infrastructure will be a combination of digital infrastructure (for instance, data collection and communication networks, data storage and sharing facilities) and physical infrastructure (for example changes to lanes, road markings, and lighting).

At the same time, widespread deployment of AVs will directly impact the wider transport infrastructure. One possible effect is a reduction in parking space needed because AVs are likely to spend less time parked and can park in smaller spaces. More distantly (and consequently with higher uncertainty) the impact that AVs have on public transport may affect the number and nature of bus stops or train stations. For example, train stations may become open spaces where AVs can deliver their passengers right up to the train’s doors.

[i] Willumsen, L. (2017) Aligning Automated Vehicles to Human Mobility. To appear in Traffic Technology International, October 2017