Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes

Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion. Dynamic toll pricing schemes effectively achieve reliable travel time on ELs. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data collected by sensors installed along the ELs. Thus, the operation of dynamic pricing critically depends on the accuracy of data collected by such traffic sensors. However, no previous research has been conducted to explicitly investigate the impact of sensor failures and erroneous sensors’ data on toll computations. This research fills this gap by examining the effects of sensor failure and faulty detection scenarios on ELs tolls calculated by a dynamic pricing algorithm. The paper’s methodology relies on applying the dynamic toll pricing algorithm implemented in the field and utilizing the fundamental speed-volume relationship to ‘simulate’ the sensors’ reported data. We implemented the methodology in a case study of ELs on Interstate-95 in Southeast Florida. The results have shown that the tolls increase when sensors erroneously report higher than actual traffic demand. Moreover, it has been found that the accuracy of individual sensors and the number of sensors utilized to estimate traffic conditions are critical for accurate toll calculations.

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.

Quantifying the Mobility Benefits of Express Lanes using Real-Time Traffic Data

Traffic congestion is one of the major problems facing transportation agencies, especially in urban areas. Agencies are exploring ways to use the existing transportation infrastructure efficiently by deploying appropriate traffic management strategies. One of these strategies is the use of express lanes, which are expected to effectively mitigate congestion and increase the reliability of highway facilities. Express lanes are managed toll lanes, separated from general-purpose lanes within a freeway facility. The goal of this study was to quantify the mobility benefits of express lanes by comparing the performance of express lanes with that of their adjacent general-purpose lanes, and by assessing the performance of the general-purpose lanes when the express lanes were open versus when the express lanes were closed. The Buffer Index (BI), a travel time reliability measure, was selected as the performance measure. The analysis was based on 95Express, express lanes along I-95 in Miami, Florida. Overall, the results indicated that BIs for the express lanes were significantly lower than the BIs for the general-purpose lanes, and the BIs for the general-purpose lanes were significantly lower when the express lanes were open compared with the periods when the express lanes were closed. The study results showed mobility improvements on both the express lanes and the general-purpose lanes, although the extent of the improvements varied by direction (i.e., northbound and southbound) and time of day (i.e., a.m. peak, p.m. peak, daytime off-peak, and nighttime off-peak). Transportation agencies may use these findings to quantify and evaluate the mobility benefits of the express lanes and the general-purpose lanes on express lane facilities.

Multiple Uses of Big Data for Model Validation and Express Lanes Traffic Forecasts

This paper highlights a variety of uses for Big Data when developing project forecasts and model validations. In most travel models, validation often refers to estimating model volumes close to the observed highway counts. While this is an established practice for producing reasonable confidence in the model, these statistics are often not sufficient to build confidence in the project forecast. This is especially true for investment-grade level traffic and revenue forecasts for projects involving congestion pricing. This paper explores the application of Big Data to validate subarea models in multiple dimensions: subarea district-to-district origin-destination (O-D) flows; corridor segment-to-segment O-D flows; and trip length distribution by O-D types for the I-4 Ultimate Express Lanes Study. Additionally, the paper reviews historical O-D flows to determine the peak seasonal flow and appropriate O-D data to use in model validation and seed tables for Origin-Destination Matrix Estimation (ODME). In addition to model validation, the expanded Big Data O-D trips were assigned to multiple paths for each O-D pair built via Google Directions API to study the eligible corridor trips and the alternative corridors competing with the project. Furthermore, spatiotemporal distributions from Big Data were used to develop time-dependent trip tables for the Dynamic Traffic Assignment (DTA) model. Several international tourist attractions located along the I-4 Ultimate Corridor serve high visitor and weekend traffic, and Big Data was used to analyze and develop a weekend distribution model. Each of these modules involves some form of observed data, all coming from one source, “Big Data.”

Awareness and Perception of Senior Citizens on the Implementation of R.A. 9994 in San Isidro in Nueva Ecija in the Philippines

This study determined the awareness and perception of senior citizens on the implementation of R.A. 9994, or the Expanded Senior Citizen Act of 2010. It employed both qualitative and quantitative research design as it described the profile of the respondents, determined the level of awareness of the respondents on the benefits granted by RA 9994, and analyzed the extent of implementation of the benefits and privileges based on R.A. 9994. The respondents came from the three clustered barangays of San Isidro, Nueva Ecija in the Philippines. The researcher used a 5-point Likert Scale questionnaire. The data gathered revealed that Senior Citizens were “Moderately Aware” of most of the parameters presented such as the 20% discount, government assistance, and priority in express lanes. On the other hand, the mean response on the extent of implementation was verbally described as “Slightly Implemented.” The Analysis of Variance showed that the level of awareness of respondents across the clustered barangays does not vary significantly. Thus, from the findings, it is recommended that a thorough study with a greater number of respondents, including the relatives/primary care giver, should be involved. Firm and systematic assessment is also recommended to monitor the implementation of the programs.