Ask the Experts–Integrated Corridor Management

What is Integrated Corridor Management (ICM) and how is it being evaluated?

We are seeing an unprecedented interest in integrated corridor management (ICM) analysis tools.  The objective of the ICM initiative is to demonstrate how intelligent transportation systems (ITS) technologies can efficiently and proactively manage the movement of people and goods in major transportation corridors.  Currently, there are three Stage 2 analysis, modeling, and simulation (AMS) evaluation sites under the United States Department of Transportation (U.S. DOT) Integrated Corridor Management Program.  These sites are Minneapolis, San Diego, and Dallas.  Caltrans has 26 Corridor System Management Plans (CSMP) under development or recently completed.  There also are related studies underway in Atlanta, New York, and other areas.

The ICM analysis efforts underway share a number of common themes.  The work begins with establishing an understanding of the area and defining the corridor.  Identifying, assessing, and selecting a simulation methodology is the second element.  Assessing existing conditions, including determining where and why congestion is occurring, represents a third element.  Replicating the existing conditions in a model is the next element.  The model is used to assess different alternatives and different combinations of alternatives.  The alternatives resulting in the best system performance and the greatest overall benefits are included in the plan.

What tools are used in ICM analysis?

There are three general types or levels of simulation analysis tools – microscopic, mesoscopic, and macroscopic.  Microscopic simulation models focus on the movement of every vehicle every second or a tenth of a second.  Microscopic simulation models are useful for assessing traffic control strategies, such as ramp metering and arterial traffic signal coordination.  Mesoscopic simulation models provide detailed analysis for sub-areas.  Mesoscopic models are used to analyze traveler information, tolling, high-occupancy toll (HOT) lanes, congestion pricing, and regional diversion.  Macroscopic travel demand models represent the traditional travel demand models that are used to assess regional travel patterns and mode shift. 

One of the lessons learned is that the traditional analysis of a “typical day” is not very useful.  Focusing the analysis on different operational scenarios is more useful.  Scenarios that should be examined include days without incidents, days with incidents, days with different weather conditions, days with high travel demand, and days with special events.  We are learning that one type of analysis does not fit all conditions and that there is a need to focus on the different operational conditions.

For example, in one test case study we examined incident patterns and travel demand in a corridor.  In this example, 25 percent of the days had major incidents together with high demand.  Approximately 22 percent of all days feature both low demand and minor incident conditions.  Approximately 40 percent of total annual delay occurred on the worst 25 percent of the days.  Targeting strategies to address these high incident days will have significant benefits.  In the test case, more than half of ICM benefit is on high demand/major incident days.

Can ICM strategies be used in combination?

Different combinations of ICM strategies are needed to address different corridor operational conditions.  Having a variety of ICM tools and strategies is important to optimize the operation of a corridor.  Various ICM strategies are being analyzed in Minneapolis, Dallas, San Diego, and other areas.  Examples in the traveler information area include earlier dissemination and information sharing between agencies, comparative travel times by mode and route, real-time parking availability at park-and-ride lots, and freeway, arterial, and transit traveler information (pre-trip and en-route).  Traffic management strategies include reducing incident times, signal retiming for arterials or frontage roads, and coordinating signal and ramp meter operation.  HOV/HOT lanes represent another strategy.  A mix of transit management strategies also are being used, including dynamic re-routing, capacity expansion for special events, arterial signal priority, light rail transit (LRT), smart parking system, and physical priority to buses on arterials.

What are the modeling and analysis challenges of ICM?

We have faced a number of challenges in modeling and analyzing ICM over the past few years.  Four of the key challenges relate to data, model calibration, modeling mode sift, and modeling traveler information.  There are a number of data challenges in ICM analysis.  Managing congested corridors in real-time requires a lot of data.  There are issues of data quality and data consistency, as well as integrating data across different operational conditions.  Data on arterial streets, transit services, and bottlenecks, queuing, and congestion are scarce.  Traffic control data, including data for signals and ramp meters, and incident data also are needed.  Data on all these items are needed to operate the system in real-time.

What have we learned from the current evaluations?

We have learned a number of things from the experience with the studies to-date.  First, there are questions on the quality, timeliness, consistency, and comprehensiveness of volume, speed, bottleneck, queuing, and congestion data.  Second, obtaining consistent data on arterials, transit, and incidents can also be challenging.  More work is needed to determine the detection layout for optimal monitoring and identification of traffic patterns on the transportation system.

We also have learned about data filtering and fusion.  There is a clear need for standards on accepting or rejecting field data and addressing data gaps or missing data.  We also need to explore methods to combine or fuse data from different technologies.  Our experience has also indicated a need to change maintenance practices to focus on the quick replacement of defective data collection equipment.  Installation and testing standards for sensor equipment also are needed.

We also have found that most models are not good at estimating mode choice for ICM.  Travel demand models do represent transit, but are not effective at estimating accurate transit travel times.  Further, very few simulation models can represent a transit network and none can dynamically adjust mode choice in real-time.  It is important to remember that short-term mode shift may be very different from long-term mode shift.  A possible solution to this issue is to use a simple pivot-point mode choice model that can work with trip tables from travel demand models, and work with accurate travel times from simulation models and using a simple geographic information system (GIS) representation of transit networks.

We still do not have a good understanding of the effect of the quality of traveler information on traveler responses or the factors influencing mode shift based on traveler information.  This lack of understanding makes modeling travel information challenging.  Possible solutions include assigning different levels of traveler accessibility to information and conducting sensitivity analysis on the results.

Lastly, it is important to remember the purpose of the analysis, which is to help invest in the right ICM strategies.  The models provide a predictive capability to help determine which combinations of strategies will be the most effective.  We also want to be able to invest with confidence and minimize conflicts or unintended consequences that would otherwise be unknown before implementation.  The models can also improve the effectiveness and success of implementation by helping to build consensus among stakeholders and to optimize implementation staging.  Finally, we want to provide long-term capabilities to continually improve implementation based on experience.

Vassili Alexiadis is a Vice President of Cambridge Systematics with more than 15 years of experience in the areas of ITS planning, evaluation, and market research; transportation operations; and the development of traffic simulation and transportation analysis tools. Dr. Alexiadis is the Manager of the Transportation Operations business line at Cambridge Systematics.