On a link-based day-to-day traffic assignment model

In this paper, we perform a rigorous analysis on a link-based day-to-day traffic assignment model recently proposed in He et al. (2010). Several properties, including the invariance set and the constrained stability, of this dynamical process are established. An extension of the model to the asymmetric case is investigated and the stability result is also established under slightly more restrictive assumptions. Numerical experiments are conducted to demonstrate the findings.     

广西高速公路生态环境保护对策研究

文章针对广西高速公路生态环境现状,分析当前高速公路环境保护工作中存在的问题,从生态学角度提出了高速公路沿线生态环境建设和保护对策。     

Surface street traffic volume and single-family house price

This study investigates the relationship between surface street traffic volume and single-family house prices in a relatively small city in the US. Hedonic price models are estimated using data from 9670 transactions that occurred between January 1998 and March 2011. It is discovered that parcels fronting or adjacent to a high-traffic street sell, on average, at an 8.1% discount compared to similar parcels that are not so situated. Restricting the analysis to parcels on or adjacent to a high-traffic street, house price and traffic volume are found to be negatively related; a doubling of volume from any particular traffic count, ceteris paribus, reduces selling price by an average of 2.1%.     

Traffic noise abatement: How different pavements, vehicle speeds and traffic densities affect annoyance levels

In this paper, annoyance ratings from traffic noise recorded on cobblestones, dense asphalt, and open asphalt rubber pavements are assessed with regard to car speeds and traffic densities. It was found that cobblestones pavements are the most annoying; also while open asphalt rubber pavement imposes less annoyance than dense asphalt it is not significantly different. Higher car speeds always lead to greater annoyance, as does higher traffic densities. LAeq and LAmax correlate well with annoyance, but loudness is the best predictor. Roughness and sharpness exhibit inconsistent interactions.     

CFD Analysis of Ship-to-Ship Hydrodynamic Interaction

A numerical study of ship-to-ship interaction forces is performed using a commercial CFD code, and the results are compared with experimental data and with the results of a panel method analysis. Two ship models have been used in the interaction forces analysis: a tug and a tanker, advancing parallel to each other with different lateral distances and two different values of the fluid depth. Computations are carried out with four different flow models: inviscid and viscous flow with the free surface modeled as a rigid wall and inviscid and viscous flow with the deformable free surface. A fair agreement was obtained with available experimental data and results obtained by panel method. The influence of viscosity in the computations is found to be comparatively weak, while the wavemaking effects may be important, at small magnitude of the horizontal clearance.     

Are we ready to embrace connected and self-driving vehicles? A case study of Texans

While connected, highly automated, and autonomous vehicles (CAVs) will eventually hit the roads, their success and market penetration rates depend largely on public opinions regarding benefits, concerns, and adoption of these technologies. Additionally, the introduction of these technologies is accompanied by uncertainties in their effects on the carsharing market and land use patterns, and raises the need for tolling policies to appease the travel demand induced due to the increased convenience. To these ends, this study surveyed 1088 respondents across Texas to understand their opinions about smart vehicle technologies and related decisions. The key summary statistics indicate that Texans are willing to pay (WTP) $2910, $4607, $7589, and $127 for Level 2, Level 3, and Level 4 automation and connectivity, respectively, on average. Moreover, affordability and equipment failure are Texans’ top two concerns regarding AVs. This study also estimates interval regression and ordered probit models to understand the multivariate correlation between explanatory variables, such as demographics, built-environment attributes, travel patterns, and crash histories, and response variables, including willingness to pay for CAV technologies, adoption rates of shared AVs at different pricing points, home location shift decisions, adoption timing of automation technologies, and opinions about various tolling policies. The practically significant relationships indicate that more experienced licensed drivers and older people associate lower WTP values with all new vehicle technologies. Such parameter estimates help not only in forecasting long-term adoption of CAV technologies, but also help transportation planners in understanding the characteristics of regions with high or low future-year CAV adoption levels, and subsequently, develop smart strategies in respective regions.     

How household transportation expenditures have evolved in Canada: a long term perspective

In recent years, increasing recognition of the challenges associated with global climate change and inequity in developed countries have revived researcher’s interest towards analyzing transportation related expenditure of households. The current research contributes to travel behaviour literature by developing an econometric model of household budgetary allocations with a particular focus on transportation expenditure. Towards this end, we employ the public-use micro-data extracted from the Survey of Household Spending (SHS) for the years 1997–2009. The proposed econometric modeling approach is built on the multiple discrete continuous extreme value model (MDCEV) framework. Specifically, in our analysis, the scaled version of the MDCEV model outperformed its other counterparts. Broadly, the model results indicated that a host of household socio-economic and demographic attributes along with the residential location characteristics affect the apportioning of income to various expenditure categories and savings. We also observed a relatively stable transportation spending behaviour over time. Additionally, a policy analysis exercise is conducted where we observed that with increase in health expenses and reduction in savings results in adjustments in all expenditure categories.     

Robust network pricing and system optimization under combined long-term stochasticity and elasticity of travel demand

Network pricing serves as an instrument for congestion management, however, agencies and planners often encounter problems of estimating appropriate toll prices. Tolls are commonly estimated for a single-point deterministic travel demand, which may lead to imperfect policy decisions due to inherent uncertainties in future travel demand. Previous research has addressed the issue of demand uncertainty in the pricing context, but the elastic nature of demand along with its uncertainty has not been explicitly considered. Similarly, interactions between elasticity and uncertainty of demand have not been characterized. This study addresses these gaps and proposes a framework to estimate nearest optimal first-best tolls under long-term stochasticity in elastic demand. We show first that the optimal tolls under the deterministic-elastic and stochastic-elastic demand cases coincide when cost and demand functions are linear, and the set of equilibrium paths is constant. These assumptions are restrictive, so three larger networks are considered numerically, and the subsequent pricing decisions are assessed. The results of the numerical experiments suggest that in many cases, optimal pricing decisions under the combined stochastic-elastic demand scenario resemble those when demand is known exactly. The applications in this study thus suggest that inclusion of demand elasticity offsets the need of considering future demand uncertainties for first-best congestion pricing frameworks.     

Activity involvement and time spent on computers for leisure: an econometric analysis on the American Time Use Survey dataset

Internet is capturing more and more of our time each day, and the increasing levels of engagement are mainly due to the use of social media. Time spent on social media is observed in the American Time Use Survey and recorded as leisure time on Personal Computer (PC). In this paper, we extend the traditional analysis of leisure activity participation by including leisure activities that require the use of a PC. We study the substitution effects with both in-home and out-of-home leisure activities and the time budget allocated to each of them. The modeling framework that includes both discrete alternatives and continuous decision variables allow for full correlation across the utility of the alternatives that are all of leisure type and the regressions that model the time allocated to each activity. Results show that there is little substitution effect between leisure with PC and the relative time spent on it, with in-home and out-of-home leisure episodes. Households with more children and full-time workers are more likely to engage in in-home and PC related leisure activities (especially during weekends). Increments in the travel time of social trips result in significant reductions in leisure time during weekdays.