Daily Vehicle Miles Traveled Research Articles

A hierarchical Bayesian spatiotemporal random parameters approach for alcohol/drug impaired-driving crash frequency analysis

Unobserved heterogeneity, which has been recognized as a critical issue in crash frequency modelling, generates from multiple sources, including observable and unobservable factors, space and time instability, crash severities, etc. However, only a very limited body of research is dedicated to distinguish and simultaneously address all these sources of unobserved heterogeneity. In this study, hierarchical Bayesian random parameters models with various spatiotemporal interactions are developed to address this issue. Selected for analysis are the yearly county-level alcohol/drug impaired-driving related crash counts data of three different injury severities including minor injury, major injury, and fatal injury in Idaho from 2010 to 2015. The variables, including daily vehicle miles traveled (DVMT), the proportion of male (MALE), unemployment rate (UR), and the percentage of drivers of 25 years and older with a bachelor's degree or higher (BD), are found to have significant impacts on crash frequency and be normally distributed in certain crash severities. Significant temporal and spatial heterogenous effects are also detected in all three crash severities. These empirical results support the incorporation of temporal and spatial heterogeneity in random parameters models.

Battery capacity design for electric vehicles considering the diversity of daily vehicles miles traveled

In this paper, we study battery capacity design for battery electric vehicles (BEVs). The core of such design problems is to find a good tradeoff between minimizing the capacity to reduce financial costs of drivers and increasing the capacity to satisfy daily travel demands. The major difficulty of such design problems lies in modeling the diversity of daily travel demands. Based on massive trip records of taxi drivers in Beijing, we find that the daily vehicle miles traveled (DVMT) of a driver (e.g., a taxi driver) may change significantly in different days. This investigation triggers us to propose a mixture distribution model to describe the diversity in DVMT for various driver in different days, rather than the widely employed single distribution model. To demonstrate the merit of this new model, we consider value-at-risk and mean-variance battery capacity design problems for BEV, with respect to conventional single and new mixture distribution models of DVMT. Testing results indicate that the mixture distribution model better leads to better solutions to satisfy various drivers.

Analysis of plug-in hybrid electric vehicles’ utility factors using GPS-based longitudinal travel data

The benefit of using a PHEV comes from its ability to substitute gasoline with electricity in operation. Defined as the proportion of distance traveled in the electric mode, the utility factor (UF) depends mostly on the battery capacity, but also on many other factors, such as travel pattern and recharging pattern. Conventionally, the UFs are calculated based on the daily vehicle miles traveled (DVMT) by assuming motorists leave home in the morning with a full battery, and no charge occurs before returning home in the evening. Such an assumption, however, ignores the impact of the heterogeneity in both travel and charging behavior, such as going back home more than once in a day, the impact of available charging time, and the price of gasoline and electricity. Moreover, the conventional UFs are based on the National Household Travel Survey (NHTS) data, which are one-day travel data of each sample vehicle. A motorist’s daily travel distance variation is ignored. This paper employs the GPS-based longitudinal travel data (covering 3–18months) collected from 403 vehicles in the Seattle metropolitan area to investigate how such travel and charging behavior affects UFs. To do this, for each vehicle, we organized trips to a series of home and work related tours. The UFs based on the DVMT are found close to those based on home-to-home tours. On the other hand, it is seen that the workplace charge opportunities significantly increase UFs if the CD range is no more than 40miles.

Cost analysis of plug-in hybrid electric vehicles using GPS-based longitudinal travel data

Using spatial, longitudinal travel data of 415 vehicles over 3–18 months in the Seattle metropolitan area, this paper estimates the operating costs of plug-in hybrid electric vehicles (PHEVs) of various electric ranges (10, 20, 30, and 40 miles) for 3, 5, and 10 years of payback period, considering different charging infrastructure deployment levels and gasoline prices. Some key findings were made. (1) PHEVs could help save around 60% or 40% in energy costs, compared with conventional gasoline vehicles (CGVs) or hybrid electric vehicles (HEVs), respectively. However, for motorists whose daily vehicle miles traveled (DVMT) is significant, HEVs may be even a better choice than PHEV40s, particularly in areas that lack a public charging infrastructure. (2) The incremental battery cost of large-battery PHEVs is difficult to justify based on the incremental savings of PHEVs' operating costs unless a subsidy is offered for large-battery PHEVs. (3) When the price of gasoline increases from $4/gallon to $5/gallon, the number of drivers who benefit from a larger battery increases significantly. (4) Although quick chargers can reduce charging time, they contribute little to energy cost savings for PHEVs, as opposed to Level-II chargers.

Estimation of Energy Use by Plug-In Hybrid Electric Vehicles

The fuel and electricity consumptions of plug-in hybrid electric vehicles (PHEVs) are sensitive to the variation of daily vehicle miles traveled (DVMT). Although some researchers have assumed that DVMT follows a gamma distribution, such an assumption has yet to be validated. On the basis of continuous travel data from the Global Positioning System for 382 vehicles, each tracked for at least 183 days, the authors of this study validated the gamma assumption in the context of PHEV energy analysis. Small prediction errors caused by the gamma assumption were found in PHEV fuel use, electricity use, and energy cost. Validating the reliability of the gamma distribution paves the way for its application in energy use analysis of PHEVs in the real world. The gamma distribution can be easily specified with few pieces of driver information and is relatively easy for mathematical manipulation. Validation with real world travel data enables confident use of the gamma distribution in a variety of applications, such as the development of vehicle consumer choice models, the quantification of range anxiety for battery electric vehicles, the investigation of the role of charging infrastructure, and the construction of online calculators that provide personal estimates of PHEV energy use.

Development of Decision Tool for Strategies to Reduce Greenhouse Gas Emissions

The National Household Travel Survey (NHTS) provides important information for the development of local and regional models to support decision making related to climate change and sustainability goals. This paper documents the use of NHTS data in the development of the Greenhouse Gas Statewide Transportation Emissions Planning (GreenSTEP) model, which forecasts estimates of greenhouse gas emissions at county and urban area levels. The model was developed to be sensitive to a broad number of policy variables and other factors that were not addressed in existing models. Because there was a lack of local and current sources of information about individuals, households, and their vehicle ownership patterns and travel behavior, GreenSTEP made use of the information in the national sample of the 2001 NHTS to estimate several model modules. The NHTS data were useful specifically in the development of modules on (a) land use characteristics, (b) vehicle ownership, (c) vehicle use [daily vehicle miles traveled (DVMT)], (d) impacts of vehicle travel costs on DVMT, (e) lightweight vehicles (bicycles, mopeds, electric bicycles, etc.), and (f) vehicle fleets (type and age). The NHTS data were particularly important for modeling the adoption and use of limited-range electric vehicles, as the data enabled estimates of trip length distributions to be made. This paper highlights the utility of the NHTS data for this modeling framework, the modifications and augmentations that were necessary, the limitations that were encountered, and the potential for the wider dissemination and use of the GreenSTEP tool because the initial estimation was made with a national sample.