Class 1 Railroads' Speed, Dwell Time, and Car Online Data
Class 1 Railroads' Speed, Dwell Time, and Car Online Data
Among the key performance metrics used to evaluate Class 1 railroad companies are train speed, terminal dwell time, and the number of cars online. These metrics have a direct impact on the efficiency and service quality of railroad transportation. The Surface Transportation Board (STB) releases this data to the public on a weekly basis, and a dashboard prepared by Supply Chain Dive makes this data more accessible and comprehensible.
Train Speed:
Train speed is measured in miles per hour across the entire system and indicates the velocity of freight movement between terminals. This metric does not include time spent at terminals, and therefore reflects the mobility of freight between terminals. High average speeds ensure the efficiency of the railroad network and timely delivery of freight. Major railroad companies such as BNSF, Union Pacific (UP), CSX, Norfolk Southern (NS), Canadian National (CN), Canadian Pacific (CP), and Kansas City Southern (KCS) regularly report this speed data.
Terminal Dwell Time:
Dwell time represents the average number of hours a car spends at a terminal. This period begins when a car is dropped off by a customer, transferred, or when a train arrives; and ends when the train departs, another transfer is made, or the car is positioned at a customer location. Short dwell times ensure the efficiency of terminal operations and enable freight to reach its final destinations quickly. Data provided by the STB allows for monitoring of these durations.
Cars Online:
The number of cars online represents the weekly total of cars on the railroad network, at customer locations or terminals, and not in storage. This metric is used to evaluate the available capacity and operational efficiency of railroad companies. High car counts may indicate growing demand or operational challenges. Supply Chain Dive's dashboard visualizes this data, helping users identify trends and potential bottlenecks.
Importance of the Data:
These metrics play a critical role in assessing the effectiveness and reliability of railroad transportation services. Particularly for supply chain managers and logistics professionals, this data provides valuable insights for making operational decisions and identifying potential issues in advance. For example, increasing dwell times may indicate terminal congestion or operational disruptions, while declining train speeds may point to infrastructure problems or planning shortcomings.
Data Source and Access:
The STB releases data received from railroad companies to the public on a weekly basis. However, raw datasets are typically large and complex. Supply Chain Dive presents this data in a more accessible and user-friendly format, enabling interested parties to quickly and easily access information. On the dashboard, users can select specific date ranges and railroad companies to conduct customized analyses.
In Summary...
The efficiency of railroad transportation has a significant impact on the overall performance of supply chains. Metrics such as train speed, terminal dwell time, and cars online are used as key indicators in measuring this efficiency. The data provided by the STB and Supply Chain Dive's user-friendly dashboard facilitate analysis of this data by industry professionals and support their operational decision-making.
Important Notes:
Train Speed: Indicates the velocity of freight movement between terminals; high speeds point to an efficient railroad network.
Terminal Dwell Time: Specifies the average time cars spend at a terminal; short durations indicate rapid operations.
Cars Online: Refers to the number of active cars on the railroad network; serves as a capacity and demand indicator.
Data Source: Data provided weekly by the STB is used to monitor the performance of railroad companies.
Dashboard: Supply Chain Dive presents this data in a user-friendly format to facilitate analysis.
Regular monitoring of these metrics helps supply chain and logistics professionals make more informed decisions and improve operational efficiency.
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