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

Liu, Jun; Khattak, Asad J.; Wang, Xin

doi:10.1016/j.trc.2015.01.028

Cited by 38 publications

(19 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To account for the variation of driving behaviors under different conditions, Liu et al and Wang et al introduced a varying acceleration threshold to identify extreme driving behaviors (Liu et al, 2014;Liu, 2015;Liu et al, 2015;Wang et al, 2015). Most previous studies investigating driver behaviors overlook the directions (longitudinal and lateral) of driving decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Liu

Khattak

2016

Transportation Research Part C: Emerging Technologies

Self Cite

104

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

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

Liu

Khattak

2016

Transportation Research Part C: Emerging Technologies

Self Cite

104

View full text Add to dashboard Cite

show abstract

“…AV use is expected to reduce crash counts and crash severities (Kockelman and Li, 2016), travel costs (in terms of travel time burdens for those who previously were at the wheel, maneuvering their vehicles through traffic, rain, and darkness), while facilitating longer-distance trip-making and travel by those with disabilities or those without licenses (Anderson et al, 2014;Fagnant and Kockelman, 2014a;Chen and Kockelman, 2015;Chen et al, 2016). If VMT and vehicle sizes do not increase much, AV technologies may also reduce energy consumption and emissions (Paul et al, 2011;Folsom, 2012;Ford, 2012;Chapin et al, 2013;Bansal et al, 2015;Liu et al, 2015;Liu and Kockelman, 2016;Reiter and Kockelman, 2016) Leob et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation

et al. 2017

Self Cite

View full text Add to dashboard Cite

This study provides a large-scale micro-simulation of transportation patterns in a metropolitan area when relying on a system of shared autonomous vehicles (SAVs). The six-county region of Austin, Texas is used for its land development patterns, demographics, networks, and trip tables. The agent-based MATSim toolkit allows modelers to track individual travelers and individual vehicles, with great temporal and spatial detail. MATSim's algorithms help improve individual travel plans (by changing tour and trip start times, destinations, modes, and routes). Here, the SAV mode requests were simulated through a stochastic process for four possible fare levels:

show abstract

“…In addition to simulation validations, empirical vehicle trajectory data was used to justify the use of regime-dependent plans in microscopic traffic simulator environment (Choudhury, 2007). While afore-mentioned studies provided valuable information about driving actions (Noble et al, 2014) and extreme driving events (Wang et al, 2015, Liu et al, 2015b, such extreme events could not be mapped to local traffic conditions due to unavailability of data. Similarly, the study by (Choudhury, 2007) focused on lane changing and freeway merging driving decisions, and not micro-level instantaneous driving decisions and the impact of local traffic conditions on instantaneous driving decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Khattak

Wali

2017

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

This is the pre-print version of the accepted article. For citation, please use:Khattak, Asad, and Behram Wali. "Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles.Abstract -Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking.Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two-and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N = 714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic threeregime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers' on-average tend to decelerate Keywords: connected vehicle, basic safety messages, instantaneous driving decisions, driving regimes, Markov-switching dynamic regressions.As a part of U.S. Department of Transportation's (USDOT) Real-Time Data Capture and Management Program, Safety Pilot Model Deployment (SPMD) in Ann Arbor, Michigan features real-world demonstration of connected vehicle safety applications, technologies, and systems by hosting approximately 3,000 vehicles instrumented with V2V and V2I communication systems (Henclewood, 2014). Altogether, 75 miles of roadway in Ann Arbor, Michigan are instrumented with roadside equipment (RSE) that are capable of communicating with appropriately instrumented vehicles, and devices via advanced communication and sensor technologies such as dedicated short-range communications (DSRC) (Henclewood, 2014...

show abstract

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

Cited by 38 publications

References 34 publications

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

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

Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Contact Info

Product

Resources

About