The implications of the sharing economy for transport

ABSTRACT

The sharing economy has gained a lot of attention in recent years. Despite the substantial growth in shared services, its impact overall on transport is unclear. This paper analyses the literature on sharing in transport and includes government and consultant reports, websites and academic journals. The drivers of ride-sharing, car-sharing, car-pooling and freight-sharing are largely economic and convenience related for participants. Trust, technology platforms and the trend to avoid ownership of assets are facilitating factors in its growth. Over-regulation, inconsistent quality of service and the need for recommendation are potential barriers. The transport journals in particular are relatively slow to research this topic with more focusing on bike-sharing than other modes of vehicle sharing. The paper discusses the impact of sharing on transport suggesting it is likely to be part of a solution to transport problems and congestion perhaps in combination with other developments such as driverless vehicles. It also warns of the dangers of over-regulation and under-regulation. The future will require holistic transport strategies that consider sharing options and will require government departments to work cooperatively.     

Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach

All developed economies mandate at least third party auto insurance resulting inW a vast global liability industry. The evolution towards semi-autonomous and eventually driverless vehicles will progressively remove the leading cause of vehicle accidents, human error, and significantly lower vehicle accident rates. However, this transition will force a departure from existing actuarial methods requires careful management to ensure risks are correctly assigned. Personal motor insurance lines are anticipated to diminish as liability shifts towards OEMs, tier 1 and 2 suppliers and software developers. Vehicle accident risks will hinge on vehicular characteristics in addition to driver related risks as drivers alternate between autonomous and manual driving modes. This paper proposes a Bayesian Network statistical risk estimation approach that can accommodate changing risk levels and the emergence of new risk structures. We demonstrate the use of this method for a Level 3 semi-autonomous vehicle for two scenarios, one where the driver is in control and one where the vehicle is in control. This approach is especially suited to use telematics data generated from the vehicle inherent technologies. We validate the efficacy of this approach from the perspective of the insurer and discuss how vehicle technology development will require a greater degree of collaboration between the insurance company and the manufacturers in order to develop a greater understanding of the risks semi-autonomous and fully autonomous vehicles.     

Sustainability and automobility among the elderly: An international assessment

In the next three decades there will a huge increase in both the absolute number of older people and in their percentage of the populationin almost all Western European countries, North America, and Australia. Most older people will have active lifestyles in which mobility and access play a major role and almost all older men and a majority of older women will be car drivers, used to the convenience and flexibility which the car provides.Using data from the US, Australia, Germany, New Zealand, Norway, and the United Kingdom, the paper shows that, in spite of cultural and policy differences, older people around the world are more likely to have a license, to take more trips, and to do so more often as the driver of a car than older people just a decade ago; they are also less likely to use public transit. These trends have a number of sustainability implications – the most obvious one is increased environmental pollution. For example, even though older people may travel less than younger drivers they may be polluting proportionately more because a) they are less likely to make as great a proportion of trips in public transit as younger people and b) the trips they do make may create more pollutants. In addition, older drivers may incur more wasted miles due to wayfinding errors and trip-scouting behavior. And when older people curtail their driving, younger family members may have to increase (or lengthen) their trip-making to provide needed services or additional transportation.While this paper stresses the environmental problems posed by an aging population, effective strategies arise from a focus on a broader definition of sustainability. The most important approach is to accept the inevitable and work to make the private car "greener" and safer. New transit service concepts and strategic community and neighborhood design and service elements can complement the development of cleaner cars.Although many of the potential strategies are not new, or can be expensive to implement, the convergence of environmental concerns with other problems arising from the automobility of the elderly – including increasing crash rates and serious loss of mobility among those unable to drive – may make these policies more politically viable than in the past.     

Electric vehicles: How much range is required for a day’s driving?

One full year of high-resolution driving data from 484 instrumented gasoline vehicles in the US is used to analyze daily driving patterns, and from those infer the range requirements of electric vehicles (EVs). We conservatively assume that EV drivers would not change their current gasoline-fueled driving patterns and that they would charge only once daily, typically at home overnight. Next, the market is segmented into those drivers for whom a limited-range vehicle would meet every day’s range need, and those who could meet their daily range need only if they make adaptations on some days. Adaptations, for example, could mean they have to either recharge during the day, borrow a liquid-fueled vehicle, or save some errands for the subsequent day. From this analysis, with the stated assumptions, we infer the potential market share for limited-range vehicles. For example, we find that 9% of the vehicles in the sample never exceeded 100 miles in one day, and 21% never exceeded 150 miles in one day. These drivers presumably could substitute a limited-range vehicle, like electric vehicles now on the market, for their current gasoline vehicle without any adaptation in their driving at all. For drivers who are willing to make adaptations on 2 days a year, the same 100 mile range EV would meet the needs of 17% of drivers, and if they are willing to adapt every other month (six times a year), it would work for 32% of drivers. Thus, it appears that even modest electric vehicles with today’s limited battery range, if marketed correctly to segments with appropriate driving behavior, comprise a large enough market for substantial vehicle sales. An additional analysis examines driving versus parking by time of day. On the average weekday at 5 pm, only 15% of the vehicles in the sample are on the road; at no time during the year are fewer than 75% of vehicles parked. Also, because the return trip home is widely spread in time, even if all cars plug in and begin charging immediately when they arrive home and park, the increased demand on the electric system is less problematic than prior analyses have suggested.     

Variability in urban driving patterns

Although it is known that driving patterns strongly affect the emission of pollutants from vehicles, existing empirical knowledge about driving patterns is limited. The first-step in this project was to find relevant parameters for describing driving patterns. These served as a basis for investigating variations in such patterns. An experimental study was carried out to compare driving patterns between and within different street-types, drivers and traffic conditions. Data were analysed using general factorial analysis of variance. Driving patterns showed very significant differences between street type and driver, and these factors had significant impact on all the parameters employed. The effect of street type was generally higher than the driver effect. Average speed and deceleration levels were lower at peak hours compared to off-peak hours. Men had higher acceleration levels than women generally and specially on one street type. The study showed no major differences in average speed for gender except for one street type where men drove faster than women. The knowledge attained in this study may be a step towards a better knowledge of driving patterns and their variation, and may provide possibilities of changing driving patterns and thus exhaust emissions from vehicles. Knowledge about driving patterns is also an essential part in efforts to improve models to calculate emission from traffic in urban environment.     

Fully autonomous vehicles: analyzing transportation network performance and operating scenarios in the Greater Toronto Area,Canada

Fully autonomous vehicles (AVs) have the potential to considerably change urban mobility in the future. This study simulates potential AV operating scenarios in the Greater Toronto Area (GTA), Canada, and assesses transportation system performance on a regional level. For each scenario, the base capacities of certain types of road links are modified to simulate the theoretical increase in throughput enabled by AV driving behavior. Another scenario examines driverless parking operations in downtown Toronto. Simulation results indicate that the increased attractiveness of freeways relative to other routes leads to slightly increased average travel distance as vehicles divert to access higher capacity road links. Average travel time is found to decrease by up to one-fifth at the 90% AV market penetration level. Concurrently, localized increases in congestion suggest that proactive transportation planning will be needed to mitigate negative consequences of AV adoption, especially in relation to induced demand for personal automobile travel.     

Delivering improved alerts,warnings, and control assistance using basic safety messages transmitted between connected vehicles

When vehicles share their status information with other vehicles or the infrastructure, driving actions can be planned better, hazards can be identified sooner, and safer responses to hazards are possible. The Safety Pilot Model Deployment (SPMD) is underway in Ann Arbor, Michigan; the purpose is to demonstrate connected technologies in a real-world environment. The core data transmitted through Vehicle-to-Vehicle and Vehicle-to-Infrastructure (or V2V and V2I) applications are called Basic Safety Messages (BSMs), which are transmitted typically at a frequency of 10 Hz. BSMs describe a vehicle’s position (latitude, longitude, and elevation) and motion (heading, speed, and acceleration). This study proposes a data analytic methodology to extract critical information from raw BSM data available from SPMD. A total of 968,522 records of basic safety messages, gathered from 155 trips made by 49 vehicles, was analyzed. The information extracted from BSM data captured extreme driving events such as hard accelerations and braking. This information can be provided to drivers, giving them instantaneous feedback about dangers in surrounding roadway environments; it can also provide control assistance. While extracting critical information from BSMs, this study offers a fundamental understanding of instantaneous driving decisions. Longitudinal and lateral accelerations included in BSMs were specifically investigated. Varying distributions of instantaneous longitudinal and lateral accelerations are quantified. Based on the distributions, the study created a framework for generating alerts/warnings, and control assistance from extreme events, transmittable through V2V and V2I applications. Models were estimated to untangle the correlates of extreme events. The implications of the findings and applications to connected vehicles are discussed in this paper.     

Autonomous vehicles in mixed motorway traffic: capacity utilisation,impact and policy implications

In upcoming years, the introduction of autonomous vehicles (AVs) will reshape the transport system. The transition from a regular to an autonomous transport system, however, will take place over many years and lead to a long period with a mixed driving environment where AVs and regular vehicles (RVs) operate side by side. The purpose of this study is to investigate how the utilisation of the road capacity degrades as a function of heterogeneity in congested motorways. The analysis is based on a dedicated traffic simulator, which enables the investigation of complex dynamic spillback from congestion while allowing for different degrees of heterogeneity. The representation of autonomous vehicles is based on a modified intelligent driver model (IIDM) presented by Treiber et al. (Phys Rev E 62(2):1805–1824, 2000) and Treiber and Kesting (Traffic flow dynamics, Springer, Heidelberg, 2013), while the behaviour of drivers of RVs relies on a stochastic version of the IIDM. Three main conclusions stand out. Firstly, it is shown that in an idealised environment in which AVs operate alone, a substantially improved capacity utilisation can be attained. Secondly, when drivers of RVs are mixed with AVs, capacity utilisation degrades very fast as a function of the share of RVs. Thirdly, it is shown that the improved capacity utilisation of AVs comes in the form of reduced travel time and increased throughput, with indications that travel time reductions are the most important. From a strategical planning perspective, the results underline that dedicated lanes are preferable to attain the positive effects of AVs. Specifically, we compare a stylised situation with three lanes with a share of 33% AVs to a situation with two regular lanes and a single dedicated AV lane. The latter represents a tripling in consumer surplus all other things being equal.

     

The joint analysis of injury severity of drivers in two-vehicle crashes accommodating seat belt use endogeneity

The current study contributes to the existing injury severity modeling literature by developing a multivariate probit model of injury severity and seat belt use decisions of both drivers involved in two-vehicle crashes. The modeling approach enables the joint modeling of the injury severity of multiple individuals involved in a crash, while also recognizing the endogeneity of seat belt use in predicting injury severity levels as well as accommodating unobserved heterogeneity in the effects of variables. The proposed model is applied to analyze the injury severity of drivers involved in two-vehicle road crashes in Denmark.The empirical analysis provides strong support for the notion that people offset the restraint benefits of seat belt use by driving more aggressively. Also, men and those individuals driving heavy vehicles have a lower injury risk than women and those driving lighter vehicles, respectively. At the same time, men and individuals driving heavy vehicles pose more of a danger to other drivers on the roadway when involved in a crash. Other important determinants of injury severity include speed limit on roadways where crash occurs, the presence (or absence) of center dividers (median barriers), and whether the crash involves a head-on collision. These and other results are discussed, along with implications for countermeasures to reduce injury severities in crashes. The analysis also underscores the importance of considering injury severity at a crash level, while accommodating seat belt endogeneity effects and unobserved heterogeneity effects.     

Connectivity’s impact on mandatory lane-changing behaviour: Evidences from a driving simulator study

The connected environment provides driving aids to help drivers making efficient and safe driving decisions. The literature to date is devoid of conclusive evidences of the connected environment’s impact on drivers’ mandatory lane-changing (MLC) behaviour. As such, the objective of this study is to examine MLC behaviour through a driving simulator experiment using the CARRS-Q Advanced Driving Simulator. Participants with diverse background performed the experiment in randomised driving conditions: baseline (without the driving aids), connected environment with perfect communication, and connected environment with communication delay. Repeated measure ANOVA in the form of linear mixed model and Generalized Estimation Equation (GEE) are employed to analyse various driving performance indicators during MLC event. We find that drivers in the connected environment tend to wait longer, increase the initial speed, and maintain a larger spacing, compared to when they are driving in the baseline condition. In addition, drivers in the connected environment are likely to reject fewer number of gaps and select relatively bigger gap sizes. Furthermore, post-encroachment time (PET) in the connected environment is higher across different gap sizes, indicating that the connected environment makes MLC safer. The GEE model on gap acceptance suggests that the perfect communication and communication delay has positive and negative impact on the accepted gap size, respectively, and the GEE model on lane-change duration indicates that lane-change duration tends to increase in the connected environment.     

Transit user perceptions of driverless buses

This paper reports the results of a stated preference survey of regular transit users’ willingness to ride and concerns about driverless buses in the Philadelphia region. As automated technologies advance, driverless buses may offer significant efficiency, safety, and operational improvements over traditional bus services. However, unfamiliarity with automated vehicle technology may challenge its acceptance among the general public and slow the adoption of new technologies. Using a mixed logit modeling framework, this research examines which types of transit users are most willing to ride in driverless buses and whether having a transit employee on board to monitor the vehicle operations and/or provide customer service matters. Of the 891 surveyed members of University of Pennsylvania’s transit pass benefit program, two-thirds express a willingness to ride in a driverless bus when a transit employee is on board to monitor vehicle operations and provide customer service. By contrast, only 13% would agree to ride a bus without an employee on board. Males and those in younger age groups (18–34) are more willing to ride in driverless buses than females and those in older age groups. Findings suggest that, so long as a transit employee is onboard, many transit passengers will willingly board early generation automated buses. An abrupt shift to buses without employees on board, by contrast, will likely alienate many transit users.

     

Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users

Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.     

The effect of the competition between cars and trucks on the evolution of the motorway transport system

Motorways, which were devised at the beginning of their history as dedicated roads intended to be traveled by cars only, are at present also traveled by considerable flows of trucks. This fact has deeply changed the motorway transport system with respect to its original conception, owing to the interactions between two categories of vehicles whose characteristics are very different. These interactions greatly increase the transport cost perceived by car drivers with respect to truck drivers. This paper studies the consequences of this cost asymmetry on the evolution of the transport system when the geometric characteristics of a motorway remain unchanged in time, while transport demand increases. By using a theoretical model of competition between cars and trucks, it is shown that, if both the geometric characteristics of a motorway and the increase rate of the activities that feed the transport demand remain unchanged over time, the competition between cars and trucks, as well as the fact that in general passengers have better transport alternatives than freight, make the increase rate of truck traffic greater than that of cars, causing a progressive increase in the proportion of trucks in the time periods in which a motorway is traveled by both the vehicle categories. Since truck traffic on motorways, at least in Europe, is very scarce on weekends and in holiday periods, in which motorways are traveled almost only by cars, these results seem to indicate a tendency to the specialization of motorways, which are likely to be used in the future mostly by only one category of vehicles in different periods of time.     

Estimating potential increases in travel with autonomous vehicles for the non-driving,elderly and people with travel-restrictive medical conditions

Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.     

The impact of transportation alternatives on the decision to cease driving by older adults in Japan

This study investigated whether the availability of transportation alternatives for older drivers is a determinant of the decision to cease driving. We recruited participants from a total of 7827 drivers aged 69 years or older living in Ibaraki Prefecture, Japan who were scheduled to renew their driving license between February and April 2011. In November 2010, we distributed questionnaires to collect data on predictors of driving cessation, and again in December 2012 to determine who had actually ceased driving. The relative impacts of factors related to driving cessation were then estimated. Of the 3089 respondents, 157 did not renew their license. The strongest determinants of this decision were having been advised to stop driving and if they had developed less confidence in their ability to drive safely. Even so, respondents were far more likely to have actually stopped driving if they were sure that someone else was available to provide a ride when they needed one. The final decision to stop driving is strongly influenced by personal convenience based on private transport, especially amongst drivers who have been advised to stop. The availability of public transport alternatives is not as important a factor in this decision.     

Mobile phone use while driving: public opinions on restrictions

This paper reviews two road-user surveys on the use of mobile phones on the road in Finland where the mobile phone ownership rate is highest in the world (70% in August 2000). From 1998 to 1999 the proportion of drivers that chose to use a mobile phone while driving rose from 56% to 68%, while the proportion of phone using drivers who experienced dangerous situations due to phone use rose from 44% to 50%. The proportion of drivers who used their phones in some way to benefit safety on the road remained at about 55%. The youngest, novice drivers had the highest level of phone usage of all age categories. Over 48% of the interviewees believed that the government should ban the use of hand-held mobile phones while driving, and another 27% believed that all types of mobile phone use should be banned while driving. Those drivers who used their phones the most each day were more likely to want some form of restrictions, than those who had lower usage. This is a strong message to the elected lawmakers and raises the problem of exactly how regulatory bodies would go about controlling the future growth of new driver support and non-driving related communication devices in road vehicles. It was concluded that legislating for hands-free use only would be a reasonable course of action. Mandating that the current generation of equipment should be optimized for hands-free use should result in future generations of in-vehicle equipment also being optimized for hands-free use as a minimum criterion.     

Distributional impacts of road pricing: The truth behind the myth

This paper shows that road pricing can be regressive, progressive or neutral, and refutes the generalised idea that road pricing is always regressive. The potential distributional impacts of a road pricing scheme are assessed in three English towns. It is found that impacts are town specific and depend on where people live, where people work and what mode of transport they use to go to work. Initial impacts may be progressive even before any compensation scheme for losers is taken into account. When the situation before the scheme is implemented is such that majority of drivers entering the area where the scheme would operate come from households with incomes above the average, it can be expected that, once the scheme is implemented, these drivers coming from rich households will continue to cross the cordon and will be prepared to pay the charge. In such a case the overall effect will be that on average, rich people will pay the toll and poor people will not.     

Automated vehicles: exploring possible consequences of government (non)intervention for congestion and accessibility

ABSTRACT

Academic research on automated vehicles (AVs) has to date been dominated by the fields of engineering and computer science. Questions of how this potentially transformative technology should be governed remain under-researched and tend to concentrate on governing the technology’s early development. We respond in this paper by exploring the possible longer-term effect of government (lack of) intervention.

The paper tests the hypothesis that a “laissez-faire” governance approach is likely to produce less desirable outcomes in a scenario of mass uptake of AVs than would a well-planned set of government interventions. This is done using two prominent themes in transport policy – traffic flow and accessibility – in a scenario of high market penetration of Level-5 automated vehicles in capitalist market economies. The evidence used is drawn from a literature review and from the findings of a set of workshops with stakeholders.

We suggest that a laissez-faire approach will lead to an increase in traffic volume as a result of a growing population of “drivers” and a probable increase in kilometres driven per passenger. At the same time, the hoped-for increases in network efficiency commonly claimed are not guaranteed to come about without appropriate government intervention. The likely consequence is an increase in congestion. And, with respect to accessibility, it is likely that the benefits of AVs will be enjoyed by wealthier individuals and that the wider impacts of AV use (including sprawl) may lead to a deterioration in accessibility for those who depend on walking, cycling or collective transport.

We consider the range of possible government intervention in five categories: Planning/land-use; Regulation/policy; Infrastructure/technology; Service provision; and Economic instruments. For each category, we set out a series of interventions that might be used by governments (at city, region or state level) to manage congestion or protect accessibility in the AV scenario described. Many of these (e.g. road pricing) are already part of the policy mix but some (e.g. ban empty running of AVs) would be new. We find that all interventions applicable to the management of traffic flow would also be expected to contribute to the management of accessibility; we define a small number of additional interventions aimed at protecting the accessibility of priority groups.

Our general finding is that the adoption of a package of these interventions could be expected to lead to better performance against generic traffic-flow and accessibility objectives than would a laissez-faire approach, though questions of extent of application remain.

In our conclusions, we contrast laissez-faire with both anticipatory governance and “precautionary” governance and acknowledge the political difficulty associated with acting in the context of uncertainty. We point out that AVs do not represent the first emerging technology to offer both opportunities and risks and challenge governments at all levels to acknowledge the extent of their potential influence and, in particular, to examine methodically the options available to them and the potential consequences of pursuing them.     

Failure rates and data driven policies for vehicle safety inspections in Pennsylvania

Rail, truck, commercial bus, and aircraft have federally mandated safety inspection programs in the United States, while inspections of personal vehicles, which make up the majority of passenger miles, are optionally imposed at the state level. In recent years, some states have chosen to eliminate the vehicle safety inspection program because of budget constraints and concerns about program effectiveness. Currently, 26 states have a schedule for conducting safety inspections, but Pennsylvania is one of thirteen states that currently require all personal light duty vehicles to be inspected every year. The remaining states have completely eliminated safety inspection programs. However, as automobiles become safer, Pennsylvania legislators are now pushing to phase out the inspection program to reduce the costs of owning a vehicle. This study combines Pennsylvania vehicle registration data with two large samples of results from state safety inspections. We find that the state safety inspection fail rate for light-duty vehicles is 12–18%, well above the often-cited rate of 2%. Vehicles that are older than three years old or have more than about 30,000 miles can have much higher rates. When analyzing new vehicles, less than or equal to one year old, it is found that even these vehicles have a failure rate greater than zero. Furthermore, while the vehicle fleet appears to be getting safer over the past few years by improvements in technology or other external circumstances, the inspection failure rate does not appear to be trending toward zero in the near future. We also show that accurate inspection data is limited and often incorrectly analyzed. Lastly, the importance of vehicle maintenance over a vehicle’s lifetime is proven to be evident, since regular usage causes vehicles to deteriorate. We conclude that vehicle safety inspections should continue to be implemented in order to keep driving conditions safe.     

The role of alternative fuel vehicles: Using behavioral and sensor data to model hierarchies in travel

Greater adoption and use of alternative fuel vehicles (AFVs) can be environmentally beneficial and reduce dependence on gasoline. The use of AFVs vis-à-vis conventional gasoline vehicles is not well understood, especially when it comes to travel choices and short-term driving decisions. Using data that contains a sufficiently large number of early AFV adopters (who have overcome obstacles to adoption), this study explores differences in use of AFVs and conventional gasoline vehicles (and hybrid vehicles). The study analyzes large-scale behavioral data integrated with sensor data from global positioning system devices, representing advances in large-scale data analytics. Specifically, it makes sense of data containing 54,043,889 s of speed observations, and 65,652 trips made by 2908 drivers in 5 regions of California. The study answers important research questions about AFV use patterns (e.g., trip frequency and daily vehicle miles traveled) and driving practices. Driving volatility, as one measure of driving practice, is used as a key metric in this study to capture acceleration, and vehicular jerk decisions that exceed certain thresholds during a trip. The results show that AFVs cannot be viewed as monolithic; there are important differences within AFV use, i.e., between plug-in hybrids, battery electric, or compressed natural gas vehicles. Multi-level models are particularly appropriate for analysis, given that the data are nested, i.e., multiple trips are made by different drivers who reside in various regions. Using such models, the study also found that driving volatility varies significantly between trips, driver groups, and regions in California. Some alternative fuel vehicles are associated with calmer driving compared with conventional vehicles. The implications of the results for safety, informed consumer choices and large-scale data analytics are discussed.