Graph and download economic data for Rail Freight Carloads (RAILFRTCARLOADSD11) from Jan 2000 to Apr 2025 about railroad, freight, and USA.

Monthly railway industry carloading statistics for intermodal and non-intermodal traffic in metric tonnes, for the period from January to the most current month of the current year, Canada, Eastern Division and Western Division.

The dataset contains weekly rail service metrics from the Surface Transportation Board (STB). The STB began collecting some service metrics from railroads in October 2014 and issued a final rulemaking in March 2017, ultimately collecting data on 11 different categories of rail service (listed below). The STB also collects Chicago-specific metrics that are not included in this dataset. The original STB source data can be found here: https://prod.stb.gov/reports-data/rail-service-data/.

This dataset is a compilation of all 11 categories in a single table. The single-table approach offers a convenient way to access all of the data at once, and it enables the use of the "global filter" feature in the Agricultural Rail Service Metrics dashboard found here: https://internal.agtransport.usda.gov/stories/s/jxpf-zf6y.

For those that wish to work with each category separately (or to view the metadata for each category), the 11 individual tables can be accessed at the following URLs:

Carloadings: https://agtransport.usda.gov/d/tb7q-kn5i. Train Speeds: https://agtransport.usda.gov/d/2wy9-nmz4. Origin Dwell Times: https://agtransport.usda.gov/d/34cn-rk65. Terminal Dwell Times: https://agtransport.usda.gov/d/9z94-b4fw. Cars On Line: https://agtransport.usda.gov/d/grdc-x6yk. Manifest Grain Car Order Fulfillment: https://agtransport.usda.gov/d/57pa-stwn. Railcars Not Moved: https://agtransport.usda.gov/d/fta3-xryx. Trains Held Short: https://agtransport.usda.gov/d/iacs-9uck. Grain Cars Loaded and Billed: https://agtransport.usda.gov/d/27k8-utc2. Grain Shuttle Train Turns: https://agtransport.usda.gov/d/nwye-ushu. Coal Unit Train Loadings: https://agtransport.usda.gov/d/5pbv-pd4d.

Graph and download economic data for Rail Freight Intermodal Traffic (RAILFRTINTERMODAL) from Jan 2000 to Apr 2025 about railroad, freight, and USA.

Weekly actual and planned coal loadings from the Surface Transportation Board's (STB) Rail Service Metrics. Railroads provide the average daily count (per week) of unit train coal traffic from major coal producing regions. They have the option of providing this metric on a train- or carload basis. For example, BNSF reports the average daily loadings for the week of coal unit trains. CSX, on the other hand, reports on a carload basis. Data are broken out by coal production region.

The Monthly railway carloadings publication provides monthly statistics of rail car loadings in Canada. The publication offers a brief analysis along with a number of tables showing car loadings and tonnes carried by 64 commodity groupings. These data are considered to be a good leading indicator of current business activity.

In-Motion Rail Scales Market size was valued at USD 417.14 Million in 2023 and is projected to reach USD 596.74 Million by 2030, growing at a CAGR of 4.68% from 2024 to 2030.

Global In-Motion Rail Scales Market Overview

Globally, major rail networks are being expanded and electrified in order to promote the most environmentally friendly mode of travel. Private companies handle the majority of the world's largest rail networks. At the same time, governments have some authority over railway network operations. Additionally, the rapid expansion of metro lines in all countries, as well as the rising network of subways in European countries, are likely to raise the demand for in-motion rail scales. With all of the coming metro developments and the expansion of the subway network, the in-motion rail scales market is likely to thrive.

However, there are several factors that can have a negative impact on the market growth. In-motion rail scales can accurately estimate a vehicle's weight without requiring it to come to a complete stop. They are the most efficient and cost-effective method for obtaining rail car weight. They can be used in routine car handling procedures. A properly installed scale system allows you to get and maintain rail car weights, car IDs, and time and date without spending any extra time or effort. However, various obstacles impede the expansion of the in-motion rail scales industry. Some in-motion scales are small, making them difficult to utilize on larger trains.

The market has a positive future outlook due to the vital infrastructure and are a main form of transportation in many nations, contributing significantly to economic development due to their comparative advantages in handling specific types and amounts of freight. As a result, governments around the world are actively emphasizing the construction of railway network infrastructures and enacting favorable policies to assist their growth, hence driving market expansion. Countries with effective freight railways gain a competitive advantage and reap additional benefits from well-balanced transportation systems that maximize the transit of various types of freight in the most appropriate mode.

The global in-motion rail scales market is experiencing robust growth, driven by increasing demand for efficient and accurate freight weighing solutions across various industries. The rising need for real-time tracking of railcar loads, particularly in sectors like bulk commodities (coal, grain, minerals) and intermodal transportation, is a primary catalyst. Furthermore, stringent regulatory compliance requirements concerning weight and load management are compelling railway operators and logistics companies to adopt advanced in-motion weighing systems. Technological advancements, such as improved sensor technologies, sophisticated data analytics capabilities, and the integration of IoT (Internet of Things) solutions, are further enhancing the efficiency and accuracy of these scales, driving market expansion. The pit-less type in-motion rail scales are witnessing higher adoption due to their ease of installation and lower infrastructure costs compared to their pit-type counterparts. Major applications include transload operations, unit train weighing, and tracking railcar loads, with the former dominating the market share due to its widespread use in various industries. While the market presents significant opportunities, certain restraints exist. High initial investment costs associated with the installation and maintenance of in-motion rail scales can be a barrier to entry for smaller businesses. Furthermore, the need for specialized expertise in installation and calibration can limit market penetration in certain regions. However, the long-term benefits of improved operational efficiency, reduced labor costs, and enhanced regulatory compliance are gradually outweighing these challenges. The market is expected to see continued growth, fueled by technological advancements and increasing demand in developing economies with expanding rail networks. Key players in the market are focusing on strategic partnerships, product innovations, and geographical expansions to gain a competitive edge. The ongoing development of more robust and reliable systems, coupled with increasing digitalization of the rail industry, promises continued expansion of this market.

The dataset provides the number of patrons that boarded and alighted a particular train service per business date. Estimated passenger counts includes all persons aged 5 and over, excluding drivers and station staff. Data users should interpret the data cautiously, as the model provides estimates only, and its algorithms rely on a series of assumptions that are listed in the Data Quality section below. In particular, greatest caution is needed in relying on estimated train loadings at busier stations where there are greater transfers between train services, such as at the City Loop station and other transfer hubs. Methodologies for the estimation of patronage are subject to continuing improvements as new technology and techniques are available, and this data may be refined and updated over time. Data Quality 1) Passenger counts is derived from a model which includes source data of manual survey counts, myki ticketing data, myki barrier data and manual conductor counts. The purpose of the patronage survey is to determine the transaction rate, which is the percentage of passengers who ‘touch-on’ when they travel. Ticketing transactions are boosted according to the transaction rate to provide an estimate of total patronage. 2) As of January 2021, metropolitan train patronage uses barrier count data, where available, in place of survey observations to determine the transaction rate. 3) Metropolitan train patronage totals are derived from a count of station entries, plus a 5% ‘transfer uplift’ to account for trips following a transfer within a station. Those reported figures will not reconcile with the totals shown here due to the differing methods and application of rounding. 4) Patronage data for regional train is provided by V/Line and is based on conductor or driver counts for each regional train and coach service. 5) Business Date is used instead of the actual date for all fields. The Day of Week and Day Type will follow the Business Date and not the calendar day. 6) Due to the granular nature of the data, the fields of ‘Passenger Boardings’, ‘Passenger Alightings’, ‘Passenger Arrival Load’, and ‘Passenger Departure Load’ have been aggregated into Bin sizes to protect passenger privacy. 7) For Direction, ‘U’ (up) denotes services travelling towards Flinders Street Station and ‘D’ (Down) denotes services travelling away from Flinders Street Station. 8) Line and Group is not differentiated for V/Line services and will have identical cells for V/Line services.

The dataset provides the number of patrons that boarded and alighted a particular train service per business date. Estimated passenger counts includes all persons aged 5 and over, excluding drivers and station staff.

Data users should interpret the data cautiously, as the model provides estimates only, and its algorithms rely on a series of assumptions that are listed in the Data Quality section below. In particular, greatest caution is needed in relying on estimated train loadings at busier stations where there are greater transfers between train services, such as at the City Loop station and other transfer hubs. Methodologies for the estimation of patronage are subject to continuing improvements as new technology and techniques are available, and this data may be refined and updated over time.

Data Quality

1\) Passenger counts is derived from a model which includes source data of manual survey counts, myki ticketing data, myki barrier data and manual conductor counts. The purpose of the patronage survey is to determine the transaction rate, which is the percentage of passengers who ‘touch\-on’ when they travel. Ticketing transactions are boosted according to the transaction rate to provide an estimate of total patronage.

2\) As of January 2021, metropolitan train patronage uses barrier count data, where available, in place of survey observations to determine the transaction rate.

3\) Metropolitan train patronage totals are derived from a count of station entries, plus a 5% ‘transfer uplift’ to account for trips following a transfer within a station. Those reported figures will not reconcile with the totals shown here due to the differing methods and application of rounding.

4\) Patronage data for regional train is provided by V/Line and is based on conductor or driver counts for each regional train and coach service.

5\) Business Date is used instead of the actual date for all fields. The Day of Week and Day Type will follow the Business Date and not the calendar day.

6\) Due to the granular nature of the data, the fields of ‘Passenger Boardings’, ‘Passenger Alightings’, ‘Passenger Arrival Load’, and ‘Passenger Departure Load’ have been aggregated into Bin sizes to protect passenger privacy.

7\) For Direction, ‘U’ (up) denotes services travelling towards Flinders Street Station and ‘D’ (Down) denotes services travelling away from Flinders Street Station.

8\) Line and Group is not differentiated for V/Line services and will have identical cells for V/Line services.

9\) For time intervals, 0 is defined as 00:00 (minutes:seconds) to 29:59 and 30 is defined as 30:00 to 59:59

10\) Non\-timetabled services that ran within the paper ticket section of the train system are not included. This affects passenger counts for V/Line services at non\-Myki stations.

11\) Services lacking a patronage data source are recorded as having zero counts. It should be noted that in actuality, the passenger counts are unknown and are unlikely to be zero.

12\) It is difficult to infer and validate the correct service a patron transferred to in locations where there are multiple transfer options, so the data’s accuracy around these services is reduced.

13\) The model is not capacity\-constrained, leading to some few services with unrealistically high service load. Such services should be assumed to be very crowded, but these reported loads should not be treated as fact.

14\) Where atypical events occurred (e.g. disruptions), the accuracy of the data is significantly reduced.

15\) Some trips between Southern Cross and Flinders Street contain ‘dummy’ in their train number field. Due to model limitations, the exact service caught by these passengers is not known, although there is reasonable confidence that these passengers travelled between Southern Cross and Flinders Street at approximately the time shown.

This dataset provides estimates of train loads during the AM and PM peak periods - derived from a survey held on Tuesdays, Wednesdays and Thursdays from 1 March 2016 to 17 March 2016.

The primary purpose of the train load surveys is to support service planning and rail timetabling. Surveys are conducted in March and September. March loads are the preferred dataset for network planning as it reflects the busiest time of year for train travel and is less subject to seasonal factors. The March loads are usually higher and provide a better gauge of rail system performance.

Concrete box subgrades constructed from reinforced concrete serve as alternatives to conventional fill subgrades, effectively addressing the scarcity of high-quality fill materials. A hybrid simulation approach that merges coupled dynamics with finite element modelling was adopted for both single-line and double-line ballastless track-box subgrade systems, enabling a comparative analysis of dynamic stress, displacement, and acceleration. The results reveal that, when the two traffic conditions are compared, the dynamic response of the concrete box subgrade under double-line opposing operation shows a marked increase, particularly when the dynamic displacement increases by 80%. Under opposing traffic conditions, the dynamic stress on the subgrade surface exhibits a "saddle" distribution. Vertically, the dynamic stress inversely increases within the roof and rapidly attenuates in the vertical web and floor, with reductions reaching 92.7% at the floor bottom, demonstrating the substantial capacity of the concrete box subgrade to disperse train loads. The peak dynamic displacements recorded at the subgrade surface are 0.178 mm for single-line traffic and 0.320 mm for opposing operations, indicating minimal overall vertical deformation of the concrete box subgrade. Notably, the dynamic displacement on the subgrade surface results primarily from the underlying weak subsoil. Vertical acceleration attenuation occurs predominantly within the vertical web depth, with attenuation rates exceeding 95%. The environmental vibrations induced by high-speed trains predominantly affect the area within 0 to 4 m from the edge of the subgrade floor.