Karl Sturm 1. Arnold Kasemsarn 1. Gerald Rawling 1. Eric Sherman 1

Transcription

1 Truck-Rail Intermodal Connector Evaluation using NPMRDS Data on a PostgreSQL Database Platform: An Illinois Case Study Bader Hafeez 1 Ph.D., PE, PTOE (Corresponding Author) Karl Sturm 1 Arnold Kasemsarn 1 Gerald Rawling 1 Eric Sherman 1 1 DAMA Consultants 4524 W Washington Blvd. Suite 2 Chicago, IL Phone: bhafeez@damaconsultants.com Paper submitted for: Presentation at 95 th Annual Transportation Research Board Meeting, Jan 2016 Word Count: 5,864 + (6 Figures, Images, and Tables * 250) = 7,364

2 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman ABSTRACT This paper describes the development and use of an open-source, geospatially enabled framework and applies that platform to the analysis of freight truck travel time data extracted from the National Performance Management Dataset (NPMRDS). The NPMRDS is provided by HERE/Navteq and the American Transportation Research Institute (ATRI) under contract with the US Department of Transportation. The analysis platform is based on the PostgreSQL database and uses the PostGIS extension to enable spatial analysis inside the database. The study team developed a methodology to combine information between the NPMRDS, National Highway Planning Network (NHPN), and the Illinois Department of Transportation IRIS statewide roadway inventory. GIS application tools and the capabilities of the PostgreSQL database addressed incompatibilities and inconsistencies between supporting data sets and allowed the study team to find, access, use, and combine data from different sources. The study used these data sources to develop roadway performance measures for truck-rail intermodal connectors and to compare these connectors with roads that had similar attributes. When compared to their roadway segments with similar characteristics, speeds on intermodal connectors often demonstrated greater uniformity and consistency. Keywords: Intermodal Connectors, NPMRDS Data, PostgreSQL

3 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman INTRODUCTION Cloud computing, the development of innovative analysis platforms based on Open Source software, and the Open Data Movement have spurred innovative advances in research and technology. According to a Presidential Executive Order from May 2013, government information [in the U.S.] shall be managed as an asset throughout its life cycle to promote interoperability and openness, and, wherever possible and legally permissible, to ensure that data are released to the public in ways that make the data easy to find, accessible, and usable. (1) The volume of data collected and available to the transportation industry provide opportunities to increase the actionable insights available to improve operations. As the industry moves from scarcity to overabundance of information, many gaps remain difficult to fill. The volume and compatibility of available information lead to particular Big Data challenges as multi-gigabyte and multi-terabyte data sets are developed by a broader range of industry actors. Transportation analysts address these challenge in unforeseen ways and overcome difficulties with utilizing variants of data sets, processing millions of GPS trace data records, adapting imprecise cell tower data, and using crowdsourced social networking data in unforeseen ways (2) (3) (4) The Federal Highway Administration (FHWA) released the National Performance Management Research Dataset (NPMRDS) to help state DOTs and other agencies to calculate performance measures and comply with MAP-21 legislation requirements (5). This data set includes probe observations data for vehicles on the National Highway System (NHS) and incorporates freight data provided by the American Transportation Research Institute (ATRI) (6). Information is summarized for each of over 11,000 statewide traffic message channels (TMCs). Each TMCs tracks traffic along a short, single directional roadway segment. The NPMRDS reports the minutes between the time a vehicle enters a segment and the time it leaves the segment. The size and scale of this data make it difficult to process and analyze using traditional desktop tools and methods. The analysis platform for this study is built on the Amazon Web Services (AWS) cloud computing service and the Open Source PostgreSQL relational database with the PostGIS spatial extension. New tools and methods were developed with this platform to create compatibility between different sources of data and provide inputs for further analysis. This paper is based on a congestion analysis study that evaluates roadway performance measures in urban, suburban, and rural areas, on expressway and surface streets, and across multiple types of users and modes in Illinois. Although detailed data is available for some of these roadway types, the study team could not identify another source that provided equivalent visibility as the NPMRDS. The study team also did not identify another data source that could validate all of the observations and measurements provided by the NPMRDS. This paper highlights the following analytical results used to characterize the performance of truck rail intermodal connectors in Illinois: Travel time for a given time period for each intermodal connector was calculated using available data on travel times of overlapping TMC segments. The travel time index and reliability index were then calculated for each intermodal segment for weekday peak periods, Saturday and Sunday. These performance measures are among the measures recommended by the AASHTO Standing Committee on Performance Management and the Texas Transportation Institute These values were also aggregated into hourly segments by day of the week, creating speed profiles for each intermodal connector. These speed profiles were then visualized on line graphs with comparable road segments. The comparable segments had similar road attributes but were not included in the same category of intermodal connectors. This paper provides background on the NHS and its designated intermodal connectors, describes the processing of NPMRDS data, and explains how open source tools and data platforms were used to construct the platform. The paper continues with a detailed description of our methods to identify and extract travel time for intermodal connectors and the implications of our findings, their limitations, and

4 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman recommendations for future work. The initial platform development work and analysis methodologies are implemented to provide a reusable foundation for further work. BACKGROUND The Chicago area is considered the largest inland port in the country. Facilities in the Chicago area handled 7.23 million intermodal shipments in The port of Los Angeles-Long Beach, the country s largest ocean container port, handled about 14.6 million TEU container units (a twenty foot container is 1 TEU; a forty foot container is 2 TEUs) in Containers arrive at an ocean port and are often transported by rail to terminals around the Chicago area. In Chicago, the containers are loaded onto truck chassis or are switched to another railroad (7). The presence and continued growth of the inland port and its associated developments create unique challenges for Chicago s transportation network and for the roads leading to these terminals (8). Many of the public roads connecting intermodal terminals and major highways are designated as National Highway System (NHS) intermodal connectors by the FHWA. State departments of transportation and metropolitan planning organizations (MPOs) nominate connectors that meet qualifying criteria. These criteria may include induced highway traffic and the importance of the terminal activity to a state s economy. The original intermodal connector inventory was created in 1999 (9). The impetus was provided by the supply chain industry s emphasis on last mile mobility issues. Intermodal connectors are currently included in the NHS inventory and are eligible for NHS funding. In Illinois, the rail to highway transition is recognized by the Illinois Department of Transportation (IDOT) 2012 Long Range State Transportation Plan as an important regional economic activity (10). This study is focused on intermodal connectors between highways and rail terminals or ramps. Data Inventory Challenges and Processing Methods Several federal, state, regional, and local sources provide information about intermodal connectors in the Chicago area. These sources were identified and retrieved to identify corresponding attributes and digitization methods. Some of these sources provided information that contradicted or differed from information available from other sources. To identify a complete and accurate inventory of intermodal connector information, some of these sources were discarded after identifying that they reported inaccurate, out-of-date, or otherwise unreliable information. The study used the IDOT IRIS inventory and the most recent National Highway Planning Network (NHPN) GIS shapefiles to describe different aspects of the intermodal connectors (11) (12). The IRIS shapefile road segments also aligned most often with the TMC segments defined by the NPMRDS. GIS tools combined attribute data from both sources for all intermodal connectors and created a final, comprehensive inventory of Illinois intermodal connectors. National Performance Management Research Dataset (NPMRDS) The National Performance Management Research Dataset (NPMRDS) is provided by Nokia s HERE Division under contract with the Federal Highway Administration (FHWA). The NPMRDS data reports individual vehicle travel times between the beginning and end of a roadway segment in each state. In the NPMRDS, individual roadway segments in each direction are given a reference number and are referred to as a TMC. This dataset is provided to Metropolitan Planning Organizations (MPOs) and state transportation agencies to help develop operations performance measurements, analyze problem areas, identify funding priorities, and evaluate the success of past and present projects. Travel times for freight trucks are collected by the American Transportation Research Institute (ATRI) and are integrated with the passenger vehicle dataset. The NPMRDS provides roadway attributes for about 12,500 TMCs and a GIS shapefile with 11,400 TMCs in Illinois.

5 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman IDOT IRIS Inventory IDOT maintains the inventory of intermodal connectors and reports this information to the FHWA. Modifications to this inventory are initiated by state DOT and are approved by the FHWA. The IDOT inventory distinguishes intermodal connector segments by the type and mode served. It also contains roadway attributes including speed limit, functional class, lane width, and Annual Average Daily Traffic (AADT) (11). NHS Inventory The FHWA provides a National Highway System shapefile with designated segments across the country. The shapefile distinguishes intermodal connectors by the NHS attribute and provides other attributes provided by state, MPO, and local agencies and by the intermodal facility operators. DATA PROCESSING Big Data Processing Decisions The large datasets used in this study required the use of high capacity analytical tools. The NRMRDS data sets track individual vehicle movements across individual road segments. The Illinois data sets tracked about 6.8 million individual movements in October 2011; the September 2014 data set, the last month of the study, expanded the record count to about 28.3 million individual movements. Standard desktop tools including Excel and Access are not able to handle these volumes at one time. The study team considered several database and analysis platforms to manage the NPMRDS data: Oracle, Microsoft SQL, and IBM DB2 are commercial relational database platforms. Although these platforms are currently enabled for cloud or Internet-based storage, the standard deployment model requires dedicated hardware and a significant software licensing fee. MySQL and PostgreSQL are the current leaders in Open Source relational databases. Open Source allows users to customize and modify many aspects of the database operation. The Open Source model also allows deployment without the payment of a software licensing fee. Cloud based application providers often provide database storage options that use MySQL and PostgreSQL. SAS, SPSS, and R are statistical analysis tools that can provide insights into data sets and allow users to identify trends, categories, outliers, summary statistics, and other details. The study team used SAS for exchange, transform, and load (ETL) functions for the initial NPMRDS data feeds. The structure of the NPMRDS data and the requirements to relate this data to GIS and spatial data, however, are more suited to analysis by relational databases than by statistical analysis tools. Hadoop, Cassandra, MongoDB, and other Big Data platforms provide analytical capabilities that are often oriented towards unstructured data or search processes. The NPMRDS, IRIS, and NHS data sources are in structured table or GIS shapefile formats. Relational databases are often more suited to the analytical requirements of structured data than these platforms. The newer Big Data platforms have setup and hardware requirements that exceeded the requirements of other applications. The study team also has years of experience with relational databases that it did not have with these other platforms. The study team concluded that an Open Source relational database with integrated spatial processing capabilities, cloud storage compatibility, and integrated scripting would provide the capabilities required for this study and could enable uses beyond the current study. The PostGIS extension for PostgreSQL was first released in January 2005 (13). Although PostgreSQL and MySQL have many of the same capabilities, PostGIS is considered a more mature and stable GIS platform than the MySQL Spatial Extensions (14). PostgreSQL also provides scripting

6 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman capabilities in several data handling languages including PL/PgSQL and Python and integration with QGIS, an Open Source GIS application. The study team also contacted the Wisconsin Traffic Operations and Safety Laboratory, a collaboration between the University of Wisconsin and the Wisconsin Department of Transportation. Research professionals from the lab provided important insights regarding the integration of NPMRDS data with GIS technologies (15). After considering several options and their tradeoffs, the study team chose PostgreSQL as the foundation platform to store and analyze the NPMRDS data set. Data Compatibility Challenges Although the NHS shapefile is derived from data provided by state DOTs, MPOs, and local agencies, the IDOT IRIS shapefile and the NHS shapefile often provided different roadway geometries and attributes for the same segments. The shapefile used to define NPMRDS TMC segments provided another interpretation of the state roadway network. The three files provided useful informative data that would be more useful as a single, integrated view. ArcGIS, PostGIS, and QGIS provide tools to group, aggregate, and define new columns to link information across spatial data sets. The methods available, however, can introduce data integrity and accuracy problems as well as additional differences that can prevent data integration. The study team used the scripting capabilities of the PostgreSQL platform to develop extract, transform, and load (ETL) tools to improve the usability and readability of the NPMRDS data set and to link together multiple data sets. The original NPMRDS data sets stored data in variables that did not match the requirements of the proposed analytical processes. ETL tools allowed the study team to adjust these variables and add details and attributes to the data set before loading the data set into the PostgreSQL database. Cloud Hosting Because of the large file sizes, the scale of the data, and the study s requirement to develop a common collaboration platform, the study team decided to host the PostgreSQL database platform on the Amazon Web Services (AWS) Internet/cloud-hosted platform. Unlike on traditional dedicated database servers, the dedicated memory, storage space, or other parameters can be changed on a virtual, cloud-based server in a matter of minutes with the click of a button on a webpage. These capabilities allowed the study team to avoid under provisioning the server and suffering lower system performance and over provisioning and wasting resources. A cloud service also allows users from different organizations to access the database securely and at all hours. DATA PROCESSING METHODS Analyzing Travel Time Data for Intermodal Connectors The NPMRDS provides travel time estimates for roadway segments of multiple types and locations around the state of Illinois. Travel time estimates are reported as the minutes between the time a vehicle enters a roadway segment, or TMC, in one direction and the time it exits the TMC. Although other sources of travel time data are available for types of roadway segments, many of these sources focus on expressway traffic and on urban arterials. Truck-rail intermodal connectors are often located outside of urban areas and are defined along surface streets rather than on expressways. Since the study addresses congestion issues in rural and urban areas and across different types of roadways, the study team focused its attention on creating useful insights from the NPMRDS. The NPMRDS reports travel times across TMC segments for all hours of the day and week and allows comparisons across time periods and roadway types. The end points of the TMCs, however, often do not match the end points defined by the IDOT IRIS or the NHPN shapefiles. The study team defined and continues to evolve methods to match the TMC geography with the IDOT IRIS and NHPN geographies.

7 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman Establishing Data Compatibility between NHPN and IDOT IRIS The NHPN and IDOT IRIS shapefiles both provide useful attributes about the same roadway segments. The segments defined by the NHS shapefile, however, do not match segments defined by the IDOT IRIS shapefile or the HERE shapefile. The study resolves these differences by identifying the closest segments using the following ArcMap GIS procedure: IDOT segments are converted from line shapes into points Segment points and attributes are spatially joined to the nearest NHS connector segment NHS attributes are spatially joined to the original IDOT line segments Identifying Intermodal Connectors Data from the NPMRDS The IDOT IRIS inventory is based on roadway centerlines and do not provide the directionality of particular segments. The NPMRDS TMC inventory often does not match the IDOT IRIS inventory and may define multiple TMCs for the same IDOT IRIS road segment. Since an intermodal connector can extend across multiple IDOT IRIS segments, the connector can also be matched with more than one TMC in a many-to-many data relationship. A given TMC could be associated with more than one intermodal connector. The study team resolved these differences by identifying the TMCs within a 200ft buffer of each intermodal connector. For each intermodal connector, the relevant TMCs are identified visually on the GIS map; intersecting TMCs are removed. The GIS tools also identify the proportion of each TMC that is within each connector s buffer. In this study, travel times and travel speeds across intermodal connectors are calculated by combining travel times from the corresponding TMCs. When a connector ends along a TMC, the travel time for the connector is calculated as a ratio of the overlap between the connector and the TMC and the total calculated TMC length. Calculation of Speed Values for Overlapping TMCs Speed estimates for intermodal connectors are often calculated from several TMC speed estimates. In this example, the intermodal connector IMC partially overlaps three TMCs (TMC 1, TMC 2, and TMC 3). These TMCs also have a varying number of measurement observations per time period (n 1, n 2, n 3).

8 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman FIGURE 1 Illustration of an overlap between TMCs and an intermodal connector. The lengths of the TMCs are l 1, l 2, and l 3, the lengths of their overlapping parts are l o1, l o2, and l o3, and the observed speeds measurements are V 1, V 2, and V 3. The estimated speeds V for each portion of the IMC is calculated as follows: v = v IMC = (( v 01 * n 1 * l o1 ) + ( v 02 * n 2 * l o2 ) + ( v 03 * n 3 * l o3 )) / ( n 1 * l o1 + n 2 * l o2 + n 3 *l o3 ) V o1, V o2, and V o3 are the estimated speeds along the TMCs Overall speeds along the overlapped portion of the intermodal connector IMC are calculated as a weighted average of the overlap speeds. The overlap lengths along each TMC, l o1, l o2, and l o3, and the number of measurements, n 1, n 2, and n 3 are used to weigh the averages as below: v o1 = ( n1 v 1 ) / (n 1 ) v o2 = ( n2 v 2 ) / (n 2 ) v o3 = ( n3 v 3 ) / (n 3 ) V or V IMC is the estimated speed along overlapped part of the intermodal connector The total length of the overlapping parts of the TMCs can be calculated below: l = lo1 + lo2 + lo3 = loi

9 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman The output from these calculations provides inputs for the following performance profiles: Connector-specific speed profiles Performance measure calculations Comparative analysis between intermodal connectors and comparable road segments The study team identified several other sources for travel time data in the State of Illinois and the Chicago-area. Two of the most comprehensive sites, TravelMidwest.com and GettingAroundIllinois.com provide Annual Average Daily Traffic (AADT) estimates, travel time estimates for expressways, and congestion condition for arterial roadways in the City of Chicago. The Chicago Data Portal also provides travel speed estimates for surface streets in the Chicago. The intermodal connectors used in this study are surface streets that are often located outside of Chicago. Only one of the connectors, IL18R_01 a roadway segment along 79 th Street, is addressed by congestion reports on TravelMidwest.com and the Chicago Data Portal. Data from these sources do not address congestion with the 5 minute intervals and segment by segment visibility provided by the NPMRDS and did not provide information to validate these calculations.

10 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman FIGURE 2: Illinois truck-rail intermodal connector locations.

11 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman FREIGHT PERFORMANCE MEASURES OUTPUT The NPMRDS allowed the study team to calculate reliability index (RI80) and travel time index (TTI) for several truck-rail intermodal connectors. The travel time index is a performance measure used by the FHWA for its Urban Congestion Report (16). It relates the average travel time during a peak period to that during free flow conditions. Smaller values will indicate greater travel time reliability. The lowest expected possible value is 1.0. This value would indicate that peak and off-peak travel times are equal. Under ideal circumstances, this value represents how congested conditions compare to an uncongested scenario. The reliability index metric is promoted by AASHTO's Standing Committee on Performance Management (17). Its definition is currently variable and can result in different values for the same conditions at two separate agencies. This flexibility makes it excellent for internal use but creates challenges when values are compared across jurisdictions. The index equals the 80th percentile of travel time divided by an agency defined threshold travel time. This denominator may equal the time it takes to traverse the identified corridor at the speed limit, at the maximum free flow speed, or at another speed that holds importance. Roads will be less reliable the larger its values are. This analysis assumes a threshold speed of 14 mph. This corresponds to the lower threshold of an LOS value of 'D' for a class III urban street. Class III streets have free flow speeds ranging between 30 and 35 mph, which match the majority of the intermodal connectors investigated here (18). Performance metric were calculated for the following time periods: AM peak (6:00-9:00), PM peak (15:00-19:00), Saturday (8:00-16:00), and Sunday (8:00-16:00). The travel time index uses the offpeak travel time. In these calculations, the off-peak travel time is the average of the travel times found between 0:00 and 04:00. Intermodal Connector ID Distance (Miles) RI80 Wkdays 06:00-09:00 RI80 Wkdays 15:00-19:00 RI80 Sat 08:00-16:00 RI80 Sun 08:00-16:00 TTI Wkdays 06:00-09:00 TTI Wkdays 15:00-19:00 TTI Sat 08:00-16:00 TTI Sun 08:00-16:00 IL4R_ IL5R_ IL14R_ IL18R_ IL22R_ IL32R_ IL34R_ IL123R_ TABLE 1 Intermodal Connector Performance Metrics: Reliability Index (RI80) and Travel Time Index (TTI). The reliability index uses a value of 1.0 for the threshold condition. Values larger than this threshold are considered to less reliable; values smaller than the threshold represent superior travel flow conditions. Lower values correspond with roads with higher speed limits. The 14 mph threshold was used for all cases. Intermodal connectors IL14R_01 displays the highest reliability index values in this study. The highest value, 1.14, signifies travel times that are 1.14 times longer on Sundays between 8:00 and 16:00 than travel times at a constant speed of 14 mph. The travel time index can fall below 1.0 if travel time is lower during peak hours than during offpeak hours. This can occur if the off peak (0:00 to 04:00) average travel speed is unexpectedly high or if the road there is little difference between peak hour and off-peak hour conditions. Traffic signals may also

12 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman have settings that are less responsive to the truck demand during the overnight period. The travel time index for connector IL22R_01 reaches 1.19 during the PM peak hour. All travel time index results for this intermodal connector exceed 1.1 in value. Connector IL34R_01 had the lowest travel time index values in this study. Reported truck speeds are lower for this connector during the overnight period than during the peak hours. COMPARATIVE ANALYSIS This portion of the study examined whether freight intermodal connectors performed significantly differently from peer groups of typologically similar comparison segments. This study used recorded travel times and speeds derived from the NPMRDS dataset. The study started by establishing comparison groups based on attributes of the freight intermodal connector road segments. Since each current intermodal connector is also a component of the IDOT IRIS shapefile, the following attributes for each segment of each intermodal connector were gathered from this data: AADT estimates, Functional class (connectors, arterials, local streets, and other identifiers) Number of lanes Segment length Route identifier and name Inventory identifier Beginning marker and ending marker Each rail, waterway, airport, and transit intermodal connector in this study was matched to a comparison group that contained other connectors with similar attributes, such as, county and township boundaries, functional classes, AADT estimates, number of lanes, and speed limits. The individual IDOT segments were matched to corresponding TMC segments. The derived list of TMC segments provided travel time and speed estimates for the comparison groups and enabled comparisons between freight intermodal connectors and comparison segments. These comparisons were used to provide additional insight into the performance and operating impacts of freight intermodal connector traffic.

13 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman The following table indicates the group name and characteristics assigned to each rail intermodal connector in this study: Connector Group Name Group Description IL04R_01 Cook_IL4R_MinA Cook County-Minor Arterial-IL4R_01 / Proviso Township AADT 24,000-38,100 / 3-6 LANES / mph SPEED LIMIT IL05R_01 Cook_IL5R_MajC Cook County-Major Collector-IL5R_01 / Cicero Township AADT 10,000-15,000 / 2-4 LANES / mph SPEED LIMIT IL14R_01 Cook_GrF_MinA Cook County-Minor Arterial-GrF / Hyde Park, Lake, South Chicago, Thornton, West Chicago AADT 27,000-31,500 / 2 LANES / mph SPEED LIMIT IL18R_01 Cook_GrE_MinA Cook County-Minor Arterial-GrE / Hyde Park, Lake, South Chicago, Thornton, West Chicago AADT 18,500-29,000 / 4-6 LANES / mph SPEED LIMIT IL22R_01 Cook_IL22R_MinA Cook County-Minor Arterial-IL22R_01 / Thornton Township AADT 10,000-17,000 / 2-4 LANES / mph SPEED LIMIT IL22R_01 Cook_Thrn_PriA Cook County-Thornton Township-Other Principal Arterial / Thornton Township AADT 17,000-32,000 / 4 LANES / mph SPEED LIMIT IL123R_01 Will_IL123R_OthA1 Will County-Other Principal Arterial-IL123R_01-Group 1 AADT 1,200-3,200 / 2 LANES / mph SPEED LIMIT IL123R_01 Will_IL123R_OthA2 Will County-Other Principal Arterial-IL123R_01-Group 2 AADT 11,400-13,400 / 3-4 LANES / mph SPEED LIMIT IL32R_01 Tazewell_IL32R_OthA Tazewell County-Other Principal Arterial-IL32R_01 AADT 23,000-26,000 / 4 LANES / mph SPEED LIMIT IL34R_01 StClair_IL34R_MinA St Clair County-Minor Arterial-IL34R_01 AADT 3,000-11,000 / 4 LANES / mph SPEED LIMIT TABLE 2 Intermodal Connectors and Comparison Group Descriptions. The attributes of connector IL123R_1 suggest that different segments have different characteristics (higher AADT, more lanes, and higher speed limit). Different parts of the connector segment along Arsenal Road are associated with two different comparable segment groups. Comparison speeds for the rail intermodal connector segments and the comparison group segments were extracted from the May 2014 to September 2014 NPMRDS data by aggregating average truck speeds by month, day of week, hour, and TMC segment. Starting with the May 2014 data set, NMPRDS released data from an expanded geography that included segments and routes that previous sets did not include. The September 2014 data set was the last NMPRDS data available when analysis started. Connector segments were aggregated into an overall weighted truck speed for each month, day of the week, hour, and freight intermodal connector using the total number of truck records and the overlap length between the connector and the TMCs as the weights. Comparison segments are aggregated into an overall weighted truck speed using the total number of truck records and the length of each TMC segment as the weights. The following speed graphs are based on the aggregated, overall weighted truck speeds for Tuesday, Wednesdays, Thursdays, Saturdays, and Sundays and every hour of the day.

14 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman 13 (a) (b) (c) (d) (e) (f) FIGURE 3a-f Speed profiles of 5 intermodal connectors and 6 comparison groups in Cook County.

15 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman 14 (a) (b) (c) (d) FIGURE 4a-d Speed profiles of 3 intermodal connectors and 4 comparison groups outside Cook County.

16 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman The results from this study suggest speeds that are often more consistent for the freight rail intermodal connectors than for the comparison segments. Although the comparison groups are aggregated from several disconnected segments, speeds across comparison groups appear to vary more than speeds for the freight intermodal connectors. In Cook County, the freight intermodal connectors in this study and the comparable group segments suggest similar speed profiles. IL18R_01, a freight intermodal connector along 79 th Street in Chicago, and IL22R_01, a freight intermodal connector to a UP yard in Dolton along Sibley Blvd. and Indiana Ave., follow patterns that are similar to their comparison groups. IL123R_1 is the Arsenal Road and Baseline Road connector to the Joliet (Arsenal) Logistics Park site in Will County. The intermodal connector speed data is derived from the same set of TMC segments along Arsenal Road. The AADT estimates divided this segment between two different comparison groups. Although both groups carry a functional class of other arterial, segments in the Will_IL123R_OthA1 comparison group have a much lower AADT than segments in the Will_IL123R_OthA2 comparison group. Hourly speed variability is greater in the Will_IL123R_OthA1 group than in the Will_IL123R_OthA2 group. Estimated hourly speeds on IL123R_1 are similar to the Will_IL123R_OthA2 comparison group. The recorded speeds from the connector leading to the Peoria & Pekin Union Intermodal Terminal in Tazewell County, IL32R_01, are slower and demonstrate more weekend variability than the speeds recorded from its comparison group, Tazewell_IL32R_OthA. Speeds on the connector leading to the Rose Lake Terminal in St. Clair County, IL34R_01, are higher and more consistent than those in its comparison group, StClair_IL32R_MinA. Speeds from the Northeast Illinois, IDOT Region I freight intermodal connectors suggest that freight traffic on the connectors encounter more predictable speeds than traffic along comparison group roadway segments. Results from Tazewell County and St. Clair County followed similar patterns to their comparison groups. These connectors, however, had a distinct speed difference (-7 to -8 mph for each hour or +5 to +6 mph for each hour) that was evident across the hours of the day. Speed profiles from connectors in Region I often had flat speed profiles. Outside of Region I, connectors suggested behavior that was similar to their comparison group but at a distinct difference that was higher or lower. Higher speeds on the connectors may suggest the importance of focused traffic and intermodal facility management to clear trucks from the connector, the impact of more mixed traffic and types of traffic on the comparison segments, or other factors that are not evident from comparing the particular attributes that identified the comparison groups.

17 Bader Hafeez, Ph.D., Karl Sturm, Arnold Kasemsarn, Gerald Rawling, Eric Sherman CONCLUSIONS AND RECOMMENDATIONS The National Performance Management Dataset (NPMRDS) provides greater scope and visibility and at a greater level of precision than data that was often generated by in-person observations or by off-vehicle sensors. The Illinois NPMRDS has grown to over 24 million records per month and tracks individual vehicles across 12,500 defined roadway segments or TMCs. The NPMRDS provides the opportunity to analyze the impact of individual incidents and the performance characteristics of roadway corridors that may not attract attention from other studies. While trying to use and analyze the data set, however, the study team quickly recognized that the size and scope of the data set exceeded the capacity of conventional desktop tools. The study team s decision to use PostgreSQL, the PostGIS extension, and the Amazon Web Services (AWS) cloud storage service arose after evaluating several options and exchanging information with other users of the NPMRDS data set. The data structure of the NPMRDS data set and the GIS shapefiles used by NPMRDS, the Illinois Department of Transportation (IDOT), and the National Highway Planning Network (NPHN) suggested a relational database platform. PostgreSQL provided a scalable and versatile database platform that could address all of the data set records with a single command and that could integrate with commonly used exchange, transform, and load (ETL) methods, a cloud storage service, and contemporary Geographic Information Systems (GIS) applications. The study team is continuing to develop its analytical methods and to identify additional applications for NPMRDS. The size and scope of the data set allows a study team to observe the implications and causes of many different roadway performance conditions. The Illinois roadway network used by the NPMRDS, however, can differ in many ways from the roadway networks defined by IDOT and by the NHPN. In this study, the intermodal connector definitions and roadway segment characteristics could differ between data sets. GIS tools could resolve some of these differences; other differences required manual comparisons between the different geographic representations. By completing these processes and identifying methods to extract and interpret results from the NPMRDS, the study team was able to analyze, summarize, and compare performance characteristics across many different types and locations of roadways. This study provided insights into the performance of the truck-rail intermodal connectors and how they may differ from other types of roadways. Understand the particular conditions when and where congestion occurs can help to identify why it occurs and the mitigation methods that could improve those conditions.

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