Travel Patterns of Frequent and Non-Frequent Users on I-66 High-Occupancy Toll Lanes and Implications for the Value of Time Estimation

Abstract

Based on a three-month toll transaction data set that includes an anonymized unique identifier for each vehicle, this paper presents an in-depth analysis of traffic volumes and tolls on the I-66 High-Occupancy Toll (HOT) express lanes in Northern Virginia. The unique identifiers allow quantification of how frequently each vehicle travels through the corridor. Vehicles observed in selected time intervals are categorized into frequent and non-frequent groups based on the total number of trips made by each vehicle. For the morning commute, the analyses show that those traveling frequently on the HOT lanes are more sensitive to high tolls and typically travel earlier in the morning to avoid higher tolls. In other words, when tolls are relatively high (e.g., over $20), the fraction of frequent users in the traffic is much smaller as compared with that of non-frequent users (e.g., 25% versus 75%). To estimate how much toll the HOT-lane users are paying per unit of travel time saved, that is, value of travel time saving (VTTS), speeds on alternative routes parallel to the I-66 corridor are computed from probe data and compared with those on I-66 express lanes. The results show that the mean VTTS is $45.37 and $61.78 for frequent and non-frequent users, respectively, during the morning peak period. Whereas for the afternoon peak, the mean VTTS is $38.14 and $37.64 for frequent and non-frequent users. The implications of the difference in these value of time distributions for dynamic tolling are discussed.

Integrated Simulation-Optimization Framework for Assessing the Impact of I-66 Dynamic Toll Pricing on Pavement Deterioration and Maintenance Decisions

“…The toll price addition to the cost function was coded based on Equation 1, with the flow rate set to be the flow rate output of the previous time interval (section.getStaFlow). This price component was converted to its travel time equivalent by dividing Equation 1 by the value of time assumed at $38/h (assumed using ranges [$38/h-$62/h] from Cetin et al [2]).…”

“…Many studies have addressed HOT or tolled facilities in general ( 3, 4, 12–14 ), and few have targeted HOT inside the Beltway ( 1 , 2 ). Most of these studies confirm the positive impact that this methodology has had on traffic distribution, congestion, and travel time reliability.…”

“…As part of the congestion pricing strategies, high-occupancy toll lanes (HOT) are becoming increasingly popular in the U.S. (Texas, California, Colorado, etc. ), mainly because of the increased proportions of single-occupancy vehicles (SOVs) in traffic streams (1)(2)(3). The idea behind the HOT strategy is to allow SOVs to use certain lanes (or road segments) at a certain cost (tolls).…”

“…In all cases, these lanes are known to bring many benefits. Other than relieving traffic congestion, they can incentivize carpooling, bring in revenue that will be used to fund other transportation projects, make use of the extra capacity when (if) previously operated as HOV lanes, or secure a high-speed alternative during congested periods (1)(2)(3)(4).…”

“…Whatever purpose they serve, the implementation of tolls is undoubtedly expected to alter the distribution of traffic across the transportation network ( 1 , 2 ). On the other hand, traffic volumes and loading have an impact on deteriorating pavement conditions ( 5 ).…”