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

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.

A Ridesharing Choice Behavioral Equilibrium Model with Users of Heterogeneous Values of Time

Travelers decide whether to participate in ridesharing based on the trade-off between the travel time and the expense. However, it is still unclear how travelers’ values of time affect their ridesharing behaviors on the congested network. To this end, a path-based ridesharing traffic assignment model was proposed by considering travelers’ heterogenous values of time. In the proposed model, travelers are divided into several classes according to their values of time, and travelers in each class choose their travel modes and routes simultaneously which cost the least. Moreover, travelers in different classes could share the same vehicle to complete their trips together in the proposed model. This paper further discusses how the high-occupancy toll lane affects travelers’ ridesharing behaviors. Numerical results show that: (1) travelers with different values of time show differences in their ridesharing behavior; (2) the single-class ridesharing traffic assignment model may miscalculate the ridesharing scale of users; and (3) building high-occupancy toll lanes plays a positive role in promoting ridesharing for travelers with heterogeneous values of time.