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.     

Anticipating new-highway impacts: Opportunities for welfare analysis and credit-based congestion pricing

Pricing of roadways opens doors for infrastructure financing, and congestion pricing seeks to address inefficiencies in roadway operations. This paper emphasizes the revenue-generation opportunities and welfare impacts of flat-tolling schemes, standard congestion pricing, and credit-based congestion pricing policies. While most roadway investment decisions focus on travel time savings for existing trips, this work turns to logsum differences (which quantify changes in consumer surplus) for nested logit specifications across two traveler types, two destinations, three modes and three times of day, in order to arrive at welfare- and revenue-maximizing solutions. This behavioral specification is quite flexible, and facilitates benefit-cost calculations (as well as equity analysis), as demonstrated in this paper.The various cases examined suggest significant opportunities for financing new roadway investment while addressing congestion and equity issues, with net gains for both traveler types. Application results illustrate how, even after roadway construction and maintenance costs are covered, receipts may remain to distribute to eligible travelers so that typical travelers can be made better off than if a new, non-tolled road had been constructed. Moreover, tolling both routes (new and old) results in substantially shorter payback periods (5 versus 20 years) and higher welfare outcomes (in the case of welfare-maximizing tolls with credit distributions to all travelers). The tools and techniques highlighted here illustrate practical methods for identifying welfare-enhancing and cost-recovering investment opportunities, while recognizing multiple user classes and appropriate demand elasticity across times of day, destinations, modes and routes.     

Public perceptions of toll roads: A survey of the Texas perspective

Like many U.S. states, Texas is experiencing shortfalls in transportation funding, along with growing needs for system improvements. Accordingly, the Texas Department of Transportation (TxDOT) is turning to tolling to bridge the funding gap. To assist planning efforts and effectively direct public information, a telephone survey of 2111 Texans was undertaken statewide to gauge public opinion on tolling issues.Some issues yielded a definite consensus among survey respondents. Over 70% agreed on attending to existing roads first, keeping existing roads toll-free, reducing tolls after construction, using revenues within the same region, charging higher tolls for trucks, not imposing SOV tolls, and maintaining the same toll rates during rush-hours. Some opinions varied by region. Austinites were more likely to support additional transportation spending, while residents of the Lower Rio Grande Valley were less supportive of raising the gas tax and of public/private partnerships. Opinions also varied with survey design. In eight places in the survey, optional text was provided or question order was modified to intentionally influence response. For two questions, support for tolling was decreased when information on personal transportation costs and higher gas tax rates in other states was offered. Ordered probit and binomial and multinomial logit models were estimated to assess the impact of demographic and travel characteristics on respondent opinions, and results for key issues are presented here. Opinions across demographic groups also were examined. The survey was successful at measuring opinions on several key tolling issues and should prove a useful tool for transportation planners and policymakers.     

What will autonomous trucking do to U.S. trade flows? Application of the random-utility-based multi-regional input–output model

Transportation - This study anticipates changes in U.S. highway and rail trade patterns following widespread availability of self-driving or autonomous trucks (Atrucks). It uses a...     

Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China

In transportation studies, variables of interest are often influenced by similar factors and have correlated latent terms (errors). In such cases, a seemingly unrelated regression (SUR) model is normally used. However, most studies ignore the potential temporal and spatial autocorrelations across observations, which may lead to inaccurate conclusions. In contrast, the SUR model proposed in this study also considers these correlations, making the model more behaviorally convincing and applicable to circumstances where a three-dimensional correlation exists, across time, space, and equations. An example of crash rates in Chinese cities is used. The results show that incorporation of spatial and temporal effects significantly improves the model. Moreover, investment in transportation infrastructure is estimated to have statistically significant effects on reducing severe crash rates, but with an elasticity of only −0.078. It is also observed that, while vehicle ownership is associated with higher per capita crash rates, elasticities for severe and non-severe crashes are just 0.13 and 0.18, respectively; much lower than one. The techniques illustrated in this study should contribute to future studies requiring multiple equations in the presence of temporal and spatial effects.

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

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: $0.50, $0.75, $1, and $1.25 per trip-mile. These fares resulted in mode splits of 50.9, 12.9, 10.5, and 9.2% of the region’s person-trips, respectively. Mode choice results show longer-distance travelers preferring SAVs to private, human-driven vehicles (HVs)—thanks to the reduced burden of SAV travel (since one does not have to drive the vehicle). For travelers whose households do not own an HV, SAVs (rather than transit, walking and biking) appear preferable for trips under 10 miles, which is the majority of those travelers’ trip-making. It may be difficult for traditional transit services and operators to survive once SAVs become available in regions like Austin, where dedicated rail lines and bus lanes are few. Simulation of SAV fleet operations suggest that higher fare rates allow for greater vehicle replacement (ranging from 5.6 to 7.7 HVs per SAV, assuming that the average SAV serves 17–20 person-trips per day); when fares rise, travel demands shift away from longer trip distances. Empty vehicle miles traveled by the fleet of SAVs ranged from 7.8 to 14.2%, across the scenarios in this study. Implications of mobility and sustainability benefits of SAVs are also discussed in the paper.     

Modeling traffic's flow-density relation: Accommodation of multiple flow regimes and traveler types

This research investigates freeway-flow impacts of different traveler types by specifying and applying a latent-segmentation model of congested and uncongested driving behaviors. Drivers in uncongested conditions are assumed to drive at self-chosen speeds, while drivers in congested conditions are assumed to take speed as given and choose a spacing (between their vehicle and the previous vehicle). Several classes of driver-vehicle combinations are distinguished in a data set based on double-loop-detector pulses and a household travel survey. These classifications are made on the basis of vehicle type and gender, leading to class estimates of speeds and spacings. The segmentation model is specified as a logit function of density, weather, and vehicle type, leading to estimates of congested-condition probabilities. Unobserved heterogeneity is incorporated in all models via common error assumptions.Results indicate that segmentation models are promising tools for traffic data analysis and that information on travelers, their vehicles, and weather conditions explains significant variation in flow data. By clarifying a greater understanding of traffic conditions and traveler behavior explains much scatter in the fundamental relation between flow, speed, and density, can assist regions in their traffic-management efforts and engineers in their design of roadway facilities. Ultimately, such improvements to travel networks should enhance quality of life.