Travel Time Reliability Reference Manual/TTRMS Tool
The SHRP 2 reliability data and analytical tools are intended to address travel time variability in one of three ways:
1) Establish monitoring systems to identify sources of unreliable travel times;
2) Identify potential solutions to cost-effectively improve reliability;
3) Incorporate consideration of travel time reliability into transportation agencies’ planning and programming framework.
This technical memorandum documents the development of a travel time reliability monitoring system (TTRMS) for the Minnesota pilot site. The development of this system followed the guidelines of the SHRP 2 Project L02 guidebook: Guide to Establishing Monitoring Programs for Travel Time Reliability (September 10, 2012).
This document details the data sources used in the development of the TTRMS for the Minnesota Pilot site. This includes:
• Travel time and traffic data;
• TTRMS database development;
• TTRMS analysis tool
Travel time and other traffic data can be accessed from TICAS. Comparing with the MnDOT data extraction tools, TICAS performs a number of additional calculations using the data from the detector stations to produce the travel time and VMT information. The TTRMS database is configured using a macro-enabled Microsoft Excel spreadsheet. This software package was identified to be the most user-friendly and data compatible for the variety of data sources under consideration. The macros that were developed for the database application assist in the organization of the various data sources to construct the database. This section describes how these features operate and how the various data sources are linked in the database.
A series of refinements were made throughout the development of the database. For example, the database allows users to specify corridor length if a shorter segment of the overall corridor is to be analyzed. In addition, it is capable of accommodating a variety of observation time bins (e.g. 1, 2, 3, 5, 10, or 15-minutes), provided the traffic data is in the corresponding format. These refinements are expected to be used to their full capabilities in later stages of this study and the findings documented in future memoranda.
Input Data Processing
This section explains how the TTRMS interprets each data source and configures it to a standardized format allowing the data to be combined in the TTRMS database.
The traffic data downloaded using TICAS determined the maximum corridor length and the analysis time interval. The start and end point was chosen based on the detector stations, and travel time and VMT output data is provided in 0.1-mile increments.
A process in the database reformatted the weather data into the appropriate length time intervals as determined by the traffic data. When the time interval from the raw weather data was greater than five minutes, the missing intervals were assigned the conditions of the previous bin until another record was available. For example in a five-minute system, if the source data interval was from 13:05 to 13:20, the travel time records for the 13:10 and 13:15 bins would be assigned the 13:05 weather record. Conversely, when multiple records were available in a single five-minute bin, the most severe weather condition was chosen to represent the bin. Table 1 ranks the precipitation type and intensity by descending order.
Table 1 Precipitation Type and Intensity Hierarchy
|Precipitation Type||Precipitation Intensity|
Crash and Incident Data
Crashes are motor vehicle collisions with other vehicles or fixed objects that result in over $1,000 of property damage or personal injury. These events are recorded by law enforcement personnel, and crash records are compiled in the Minnesota Department of Public Safety (DPS) database. Incidents are any other non-recurring disruption to the highway that has the potential to affect capacity and throughput. These situations can include stalled vehicles, medical emergencies, or animals and debris that are on the roadway.These are frequently used to perform safety reviews on highways by computing historical crash rates to identify high crash locations.For crash data, the duration was added to the start time to determine when the crash had cleared. If the crash spanned multiple time intervals, all of the time bins contained in the duration were assigned with the crash details. Similar to the weather data, if crashes overlapped, the “worst” crash (based on severity) was selected to populate the affected time bins. A similar process was used for the incident data, with “Incident Impact” used as the hierarchy variable.
Events defined in the TTRMS tool are sports games, concerts, state fairs that have significant impacts on traffic. When an event was taking place, all time bins in the arrival and departure windows were marked with the name of the event type. Each event type was assigned name, such as “Twins_A” (for arrival) or “Vikings_D” (for departure). For instances with multiple events taking place, the names of events were combined to reference both events. For example, if a Twins game arrival overlapped a Vikings game arrival, the event would be categorized as “Twins_A_Vikings_A”.
Road work is defined as any agency activity to maintain or improve the roadway that may result in impacts to capacity. This may include short-term activities such as guardrail, sign, or lighting repair as well as more significant, long term construction actions.Road work records were applied to the TTRMS records using a similar protocol as the crash, incident, and event conditions. The input data included start time, end time, and impact attributes. All travel time records during the periods that the road work was active were assigned with the impact category.