Congestion pricing is among the most effective mechanisms to manage demand for scarce infrastructure. It has been effective in addressing congestion on roadways in urban cores, providing reliable travel times on freeways, and is frequently used in mass transit systems to manage rider demand. Trends in new mobility, most notably growth in the shared economy and the emergence of Mobility-as-a-Service, could fundamentally change how drivers use and pay for public infrastructure. This session features new research with implications for how congestion pricing is implemented and its effectiveness within a new mobility landscape that is shared, integrated and multi-modal.
Estimating the Earnings from Peer-to-Peer Carsharing for Vehicle Owners on the Turo Platform Using Anonymized Data
Joseph Schwieterman, DePaul UniversityShow Abstract View Presentation
Christopher Smith, DePaul University
Peer-to-peer carsharing, in which “hosts” (i.e., vehicle owners) make their vehicles available for a fee, has grown markedly in recent years. Little is known about how activity in this sector is distributed across communities with different socioeconomic or demographic profiles, or about the income it provides to hosts. To offer insights into these issues, this study evaluates anonymized data of trips made on Turo, one of the country’s largest peer-to-peer carsharing platforms in Illinois. It shows that usage is heaviest in higher-density neighborhoods with above-average unemployment and rental housing rates, with a particularly large concentration on Chicago’s near north, south, and west sides, as well as zip codes with sizeable minority populations. Most transactions are financially remunerative to hosts who would own their vehicle irrespective of their decision to share. When maintenance and other expenses are taken into account (while nonmonetary costs such as the host’s time are excluded), 94.9% of trips cover their marginal cost to the host. The returns from sharing SUVs tend to be higher than those for sedans and minivans. A low-income family making $40,000 annually will increase household income by 6% by sharing a vehicle 90 days annually.
Taxing Ridehailing Services: Revenue Usages, Pricing Schemes, and Media Discussions
Zhirong Zhao, University of Minnesota, Twin CitiesShow Abstract
Camila Fonseca, University of Minnesota
Raihana Zeerak, University of Minnesota
Shared mobility is transforming transportation in major urban cities. In this paper, we focus on taxes and fees on ride-hailing services, particularly those revenue strategies levied upon their usages, generally on a per trip basis, as they have a direct impact on users. We analyze these revenue strategies from three main aspects. First is the usage of the revenue. The majority of localities use them as a mechanism to cover regulatory costs or fill budget gaps, with very few using proceeds to improve transportation systems and mobility overall. Second, we analyze the different pricing schemes used across localities. Most localities adopted fixed fee/surcharge payed per trip; only two localities established differential fees depending on the type of ride, aiming to increase vehicle occupancy and reducing traffic congestion. Lastly, we conduct a media analysis to examine the rationale for imposing a revenue raising strategy, perceptions of key stakeholders, and ongoing discussions. Most debates around the adoption of the revenue raising strategy involved the legislative and executive branches of governments at different level, TNC companies, and Taxi business, etc. Supporters argued that the measure contribute to customer safety and the enhancement of equitable transportation options for all residents, while opponents stated concerns about the disproportionate impact on the middle class and low-income populations. Our findings provide a framework of current practices to assist state and local governments to make informed decisions regarding TNC taxes and regulations.
Toll Avoidance at Highways and Utility of Alternative Routes; Evidence from Highway Drivers in Greece
Ioannis Politis, Aristotle University of ThessalonikiShow Abstract
Michalis Kyriakoglou, Aristotle University of Thessaloniki
Georgios Georgiadis, Aristotle University of Thessaloniki
Panagiotis Papaioannou, Aristotle University of Thessaloniki
Road tolling plays a significant role on highway’s financial sustainability since it consists the major revenue source. This paper aims to examine the factors that affect the drivers’ route choice and urge them to avoid toll roads when an alternative free-toll route is available. The paper presents the results of a case study that is dealing with the issue of toll avoidance at the last non-privatized highway of Greece, the Egnatia Odos road. Data from a combined revealed and stated preference survey were collected and binary choice models were built for car and truck drivers so as to determine the utility of alternative routes. The results show that travel cost and toll fees are critical route choice criteria for car drivers while travel time is a key decision factor for truck drivers. The high safety standards for the toll route were appreciated by both categories of drivers. Additional trip and personal characteristics, such as gender, trip frequency, type of transported cargo and total trip length also affect drivers' choices. The elasticity of travel time and cost was estimated in order to shed light on drivers’ sensitiveness to the route attributes and it was found that truck drivers' choices are greatly influenced by their working time schedules. These findings highlight the key factors that influence the utility of toll roads and therefore could assist highway authorities and concessionaires into developing successful toll pricing policies which will not act as a detterent to the use of highways.
Traffic and Welfare Impacts of Credit-Based Congestion Pricing Applications: An Austin Case Study
Weijia Li, Chang'an UniversityShow Abstract View Presentation
Kara Kockelman, University of Texas, Austin
Yantao Huang, University of Texas, Austin
To dramatically reduce traffic congestion, improve road operations, and benefit many travelers, this paper applies policies of credit-based congestion pricing (CBCP) across the Austin, Texas regional network. Scenarios evaluated include those selecting links with maximum delays, by variably tolling bridges, and by recognizing congestion externalities across all links. Travel demand models with full feedback are used to deliver inputs for normalized logsum differences to quantify and compare consumer surplus changes across traveler types, around the region. Results suggest that limited tolling locations under four broad times of day can do more harm than good, unless travelers shift out of the PM and AM peak periods. When using CBCP across all congested links at congested times (10% of revenues will be used as administrative costs)neglecting system administrative costs – how about letting those be 20 ct/day/person? How much money would that raise to administer the system each month?) of day, an average benefit of $1.61 per licensed driver per weekday is estimated, with almost all travelers benefiting, 95.04% TAZs’ VOTT group 1 will benefit from the CBCP.