Counting vehicle miles traveled: What can we learn from the NHTS?

2021 ◽  
Vol 98 ◽  
pp. 102984
Author(s):  
Anna Alberini ◽  
Lavan Teja Burra ◽  
Cinzia Cirillo ◽  
Chang Shen
Keyword(s):  
Vehicle Miles Traveled

Vehicle Miles Traveled in Multivehicle Households

2005 ◽  
Vol 1926 (1) ◽  
pp. 198-205
Author(s):  
Somchai Pathomsiri ◽  
Ali Haghani ◽  
Paul M. Schonfeld
Keyword(s):  
Least Squares Method ◽  
Substitution Effect ◽  
Mitigation Measures ◽  
Vehicle Miles Traveled ◽  
Emission Mitigation ◽  
Conservation Policies ◽  
Multiple Vehicles ◽  
Single Vehicle ◽  
Household Travel ◽  
Behavior Shift

Vehicle miles traveled (VMT) is an important factor in the development of transportation plans, emission mitigation measures, and energy conservation policies. Therefore, estimation of VMT is a crucial task supporting such plans and policies. This research addresses the estimation of VMT in households owning multiple vehicles. This sector is expected to use vehicles differently from single-vehicle households because usage of any vehicle may depend on usage of other vehicles. Previous studies concluded that there is a substitution effect between usages of two vehicles (i.e., greater usage of one vehicle lessens usage of the other). In view of more recent changes in sociodemographic structure, the problem was revisited with the 2001 National Household Travel Survey database. The proposed VMT model is a system of simultaneous equations. Each equation explains the VMT for one of the household's vehicles. The three-stage least-squares method was used to estimate the coefficients. A case study of two-vehicle households was investigated. The resulting model shows that VMT can be explained by variables such as the vehicle's newness, number of potential car users in a household, and household income. Surprisingly, the results show not a substitution effect but a spilling effect. The VMT of the first vehicle does not depend on how much the second vehicle is driven. However, increased use of the first vehicle tends to spill over and increase the use of the second one. Some explanation of this behavior shift is provided.

Measuring Congestion and Reliability Impacts of Safety Projects

2021 ◽  
pp. 036119812110067
Author(s):  
Simona Babiceanu ◽  
Sanhita Lahiri ◽  
Mena Lockwood
Keyword(s):  
Performance Measures ◽  
Positive Impact ◽  
Operating Conditions ◽  
Vehicle Miles Traveled ◽  
Data Set ◽  
Data Points ◽  
Practical Recommendations

This study uses a suite of performance measures that was developed by taking into consideration various aspects of congestion and reliability, to assess impacts of safety projects on congestion. Safety projects are necessary to help move Virginia’s roadways toward safer operation, but can contribute to congestion and unreliability during execution, and can affect operations after execution. However, safety projects are assessed primarily for safety improvements, not for congestion. This study identifies an appropriate suite of measures, and quantifies and compares the congestion and reliability impacts of safety projects on roadways for the periods before, during, and after project execution. The paper presents the performance measures, examines their sensitivity based on operating conditions, defines thresholds for congestion and reliability, and demonstrates the measures using a set of Virginia safety projects. The data set consists of 10 projects totalling 92 mi and more than 1M data points. The study found that, overall, safety projects tended to have a positive impact on congestion and reliability after completion, and the congestion variability measures were sensitive to the threshold of reliability. The study concludes with practical recommendations for primary measures that may be used to measure overall impacts of safety projects: percent vehicle miles traveled (VMT) reliable with a customized threshold for Virginia; percent VMT delayed; and time to travel 10 mi. However, caution should be used when applying the results directly to other situations, because of the limited number of projects used in the study.

scholarly journals Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019 ◽  
Vol 11 (23) ◽  
pp. 6643 ◽  
Author(s):  
Lee ◽  
Guldmann ◽  
Choi
Keyword(s):  
Negative Binomial ◽  
Age Groups ◽  
Urban Environments ◽  
Vehicle Miles Traveled ◽  
Crash Frequency ◽  
Rural Environments ◽  
Lower Likelihood ◽  
Conditional Autoregressive ◽  
The Relationship ◽  
Binomial Models

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

scholarly journals Macroscopic dynamics and the collapse of urban traffic

2018 ◽  
Vol 115 (50) ◽  
pp. 12654-12661 ◽  
Author(s):  
Luis E. Olmos ◽  
Serdar Çolak ◽  
Sajjad Shafiei ◽  
Meead Saberi ◽  
Marta C. González
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Spatial Dynamics ◽  
Urban Traffic ◽  
Nonequilibrium Phase Transition ◽  
Vehicle Miles Traveled ◽  
The Road ◽  
Road Capacity ◽  
Total Vehicle ◽  
The Individual

Stories of mega-jams that last tens of hours or even days appear not only in fiction but also in reality. In this context, it is important to characterize the collapse of the network, defined as the transition from a characteristic travel time to orders of magnitude longer for the same distance traveled. In this multicity study, we unravel this complex phenomenon under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time τ that takes a representative group of commuters to arrive at their destinations once their maximum density has been reached. While this time differs from city to city, it can be explained by Γ, defined as the ratio of the vehicle miles traveled to the total vehicle distance the road network can support per hour. Modifying Γ can improve τ and directly inform planning and infrastructure interventions. In this study we focus on measuring the vulnerability of the system by increasing the volume of cars in the network, keeping the road capacity and the empirical spatial dynamics from origins to destinations unchanged. We identify three states of urban traffic, separated by two distinctive transitions. The first one describes the appearance of the first bottlenecks and the second one the collapse of the system. This collapse is marked by a given number of commuters in each city and it is formally characterized by a nonequilibrium phase transition.

Evaluation Methods for Estimating Vehicle Miles Traveled with GPS Travel Survey Data

2015 ◽  
Vol 2495 (1) ◽  
pp. 112-120 ◽  
Author(s):  
Alejandro Blei ◽  
Kazuya Kawamura ◽  
Mahmoud Javanmardi ◽  
Abolfazl Mohammadian
Keyword(s):  
Survey Data ◽  
Evaluation Methods ◽  
Vehicle Miles Traveled ◽  
Travel Survey

scholarly journals Model Contrast of Autonomous Vehicle Impacts on Traffic

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Derek Hungness ◽  
Raj Bridgelall
Keyword(s):  
Autonomous Vehicles ◽  
Travel Demand ◽  
Autonomous Vehicle ◽  
Sociodemographic Factors ◽  
Vehicle Miles Traveled ◽  
The Road ◽  
Traffic Conditions ◽  
Demand Models ◽  
Wide Range ◽  
Potential Benefits

The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many long-range transportation plans do not yet account for their eventual deployment. To address some of these uncertainties, this work modified an existing model for Madison, Wisconsin. To compare outcomes, the authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. However, there are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit.

scholarly journals High Emitter Light Duty Vehicle Contributions to On-road Mobile Emissions in 2018 and 2030

2015 ◽  
Vol 2 (1) ◽  
pp. 47 ◽  
Author(s):  
Susan Collet ◽  
Toru Kidokoro ◽  
Yukio Kinugasa ◽  
Prakash Karamchandani ◽  
Allison DenBleyker
Keyword(s):  
Air Quality ◽  
Environmental Protection Agency ◽  
Protection Agency ◽  
Vehicle Miles Traveled ◽  
Improvement Strategy ◽  
Quality Improvement Strategy ◽  
Light Duty ◽  
Air Quality Improvement ◽  
Light Duty Vehicle ◽  
Tier 3

Quantifying the proportion of normal- and high-emitting vehicles and their emissions is vital for creating an air quality improvement strategy for emission reduction policies. This paper includes the California LEV III and United States Environmental Protection Agency Tier 3 vehicle regulations in this projection of high emitter quantification for 2018 and 2030. Results show high emitting vehicles account for up to 6% of vehicle population and vehicle miles traveled. Yet, they will contribute to over 75% of exhaust and 66% of evaporative emissions. As these high emitting vehicles are gradually retired from service and are removed from the roads, the overall effect on air quality from vehicle emissions will be reduced.

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