Mitigating Traffic Congestion on I-10 Mississippi River Bridge in Baton Rouge, LA

The purpose of this study was to identify and evaluate treatments and strategies to mitigate traffic congestion on the I-10 Mississippi River Bridge in Baton Rouge, LA. Based on VISSIM simulation results, combination of supplyand demand-oriented measures will be required. Rehabilitation of the old bridge on US-190 and the existing US-190/US-61 corridor, overall demand management of I-10 EB traffic, reduction in percent trucks traveling eastbound on I-10 during the A.M. peak, and ramp metering at the onramp west of the I-10 Mississippi River Bridge appear to be feasible and effective solutions.


Introduction
I-10 is the most heavily traveled coast-to-coast interstate in the U.S. It is also a national freight corridor of significant importance to the economy. The portion of I-10 through Baton Rouge, LA was constructed in the 1960's as a four-lane freeway (two lanes per direction). Traffic demand in Baton Rouge has been growing steadily and currently exceeds the capacity of the corridor during the morning and afternoon peak periods. Severe traffic congestion is a recurring problem particularly along the segment of I-10 from Louisiana Highway 415 (LA 415) across the Mississippi River Bridge (MRB) to the I-10/I-12 merge. Capacity improvements to I-10 and I-12 in the eastern suburbs helped deliver more traffic to the core of the urban area, thus compounding the traffic congestion on I-10 and the MRB. Figure 1 illustrates the existing I-10 traffic issues in Baton Rouge. The I-10 MRB is the only freeway link connecting east and west Baton Rouge areas. An older bridge on the US-190/US-61 corridor is located 4.5 miles upstream of the I-10 MRB. Travelers choose to use the I-10 MRB because of ease of access to several significant locations in Baton Rouge and the poor condition of the roadway network leading to the old bridge. Relieving congestion on the I-10 MRB is challenging especially that capacity expansion is an expensive solution. For instance, a new bridge could significantly reduce congestion; however, it will cost around $1 billion [1]. Therefore, other solutions such as Active Traffic Management (ATM), Intelligent Transportation Systems (ITS), and the application of Travel Demand Management (TDM) techniques must be investigated. As such, this study focused on evaluating several supply-oriented and demand-oriented solutions to mitigate the congestion problem at the I-10 MRB site.

Previous studies
Several studies have been conducted in the past 20 years to address traffic congestion on I-10 in Baton Rouge by adding capacity and eliminating bottlenecks. These studies can be grouped into two broad groups: 1) I-10 corridor projects, and 2) off-corridor projects. Examples of the I-10 corridor studies include the I-10 Baton Rouge Major Investment Study [2], the I-10 Corridor Improvements Stage-0 Feasibility Study [3], and the Baton Rouge Loop [4]. Proposed capacity improvements included widening of I-10 by adding a third lane in each direction and making major geometric improvements to merge, diverge, and weaving segments along the 3.5mile corridor between the Interstate 10/12 split interchange and the I-10 MRB [5].
A notable example of the off-corridor projects that have been proposed is the Baton Rouge Urban Renewal and Mobility Plan (BUMP) envisioned by AECOM [6]. The BUMP project can effectively reduce the demand on the I-10 corridor through the core urban area and across the I-10 MRB. Figure 2 illustrates the proposed alignment of the BUMP project. Under the AECOM proposal, the existing US-190/US-61 corridor is utilized to construct a 60-70 mph toll-road connecting I-10 in West Baton Rouge Parish with I-12 and I-10 in East Baton Rouge Parish. Traffic demand will be shifted from the existing heavily congested I-10 MRB to the underutilized old US-190 Bridge just 4.5 miles upstream. Access to existing business and other land-uses along the US-190/US-61 corridor will be maintained through a toll-free system of frontage roads using existing right of way. In addition to improving regional mobility, the BUMP project was planned to provide an urban renewal stimulus for the older part of the US-61/US-190 corridor north of Florida Boulevard in East Baton Rouge Parish.
The supply-oriented proposals discussed earlier (both I-10 corridor projects and off-corridor projects) have not been realized for various reasons including lack of political and/or community support, lack of funding, right of way issues, and environmental impacts. While the stakeholders and leadership in Baton Rouge keep pondering these potential supply-oriented improvements, traffic demand keeps increasing and traffic congestion keeps getting worse.

Research methodology
The research methodology involved: 1) identification of potential data sources to model the I-10 MRB using microsimulation, 2) calibration, and validation of the simulation platform required for the study, 3) development of treatments and strategies for testing using the simulation platform, and 4) evaluating the effectiveness of proposed solutions. Following is a discussion of the different steps of the research methodology.

Identification of data sources
The research team identified four main sources of data: the Louisiana Department of Transportation and Development (LADOTD), the Streetlytics database developed by CitiLabs, the National Performance Management Research Data Set (NPMRDS) maintained by FHWA, and traffic signal timing data (TSTD) from the City of Baton Rouge and LADOTD. A partial VISSIM simulation model acquired from the LADOTD along with the encoded hourly volumes served as the primary data source. Data obtained from the other sources (Streetlytics, NPMDRS, and TSTD) were used for model calibration and validation. Table 1 summarizes the data available in this study.

Simulation model calibration and validation
The objective of this step was to have a working model that matches the real-world traffic conditions to the extent possible. After several runs and investigation of all intersections and roadway segments in the model, it was clear that in addition to the regular calibration process which includes adjustment of car following and lane change parameters, routing decisions were another important parameter that needed to be tackled. This was based on comparison of travel time data produced by the model with those obtained from Streetlytics and NPMRDS.

Proposed solutions
Several solutions for mitigating traffic congestion on the I-10 MRB were identified and tested using the VISSIM simulation model. These solutions can be classified as supply-oriented or demand-oriented solutions. Table 2 presents summary of the key strategies and treatments included in the simulation scenarios. A total of 10 simulation runs were performed for every proposed solution.

Evaluating the effectiveness of proposed solutions
For every solution, several measures of effectiveness (MOEs) and metrics were determined from the simulation results. The MOEs included delay, throughput, speed, vehicle-miles traveled (VMT), delay cost, and economic savings compared to the baseline conditions (do-nothing alternative). Delay cost was computed by multiplying the delay (vehicle-hours) times one-half of the average hourly wage in Baton Rouge (0.5 x $21.38/hour = $10.69/hour). For every proposed solution, the average of the 10 simulation runs was computed for every MOE for further analysis and evaluation.

Findings, conclusions, and recommendations
Results of the VISSIM simulations of the proposed solutions are summarized in the project's final report [7]. Table 3 presents comparison of the effectiveness of the different strategies and treatments included in this study. Based on the results of the simulation scenarios, the following conclusions have been reached: • A combination of supply-oriented and demand oriented strategies/treatments must be implemented to relieve congestion on I-10 in Baton Rouge. The results of this study should be considered with an important caveat in mind: the data available to the researchers for calibrating and validating the simulation model were very limited. As such, additional data on key traffic parameters (e.g., flow rate, average speed, and traffic density) as well as demand-related data (e.g., traveler preferences and perceptions, route choice, mode choice, etc.) are required to confirm the numerical values of the MOE's and metrics included in the report.