Case Study


Company Name: Port of Trois-Rivières
Date: May 2021

As one of 17 Canadian Port Authorities, and active since 1882, the Port of Trois-Rivières is a crucial player in regional, national and international economic development for major industrial sectors such as the aluminum industry, forestry, and agri-food. Strategically located halfway between Montreal and Quebec City, the Port of Trois-Rivières welcomes 55,000 trucks, 11,000 railcars, and more than 250 merchant and cruise ships annually, originating from over 100 different ports in more than 40 countries around the world. It handles over 3.3 million metric tons of goods, has an annual economic impact of nearly $2.9 billion, and supports more than 2000 direct, indirect, and induced jobs.


Nowadays ships are carrying much more cargo and ports invest in more cargo terminal capacity, which makes ports busier than ever. An increased amount of freight arriving simultaneously in ports causes more port traffic, which leads to longer wait times for trucks, ultimately leading to increased port gate-times and turn-times and often increasing port-city traffic congestion and Greenhouse Gas (GHG) emissions. These issues compel port authorities to search for new ways to manage traffic congestion near and within the ports.

A truck traffic monitoring system for ports can provide valuable decision ready data regarding truck traffic movements. The tracking data can provide useful information and Key Performance Indicators (KPIs) that can be used to manage truck movement efficiently and reduce congestion at a port.

The Port of Trois-Rivières in Quebec, Canada, offers a wide range of facilities and services to the marine related logistics industry. With an approximate surface storage area of 457,000 m2, the Port of Trois-Rivières holds several specialized terminals that can accommodate dry bulk and general cargo, both indoors and outdoors, as well as liquid bulk. The truck traffic within the Port consists of numerous truck routes stemming from two entrances, where trucks pass through scales to weigh bulk products and then proceed to product storage locations. The Port’s current truck activity data is based on information derived from the two entree gates and punctual truck traffic reports; hence, more in-port traffic behaviour information is needed to better manage truck traffic in support of plans for port expansion.


The high-level objectives of this project are:

  • To provide decision-making tools (KPIs) to gain more precise knowledge of traffic flow and improve overall port efficiency;
  • To reduce truck transit time and GHG within the Port boundaries and optimize the use of existing assets;
  • To improve traffic safety using real-time truck locations.

To achieve the project objectives SMATS’ vehicle detection technology was deployed. The solution is used to monitor truck traffic and provide key performance measures, including truck queue times at different routes, pick up and drop off idle-times, and movement patterns for real-time decision making and historical analysis.

The technology includes the installation of SMATS TrafficXHub sensors at strategic locations at the Port. These sensors capture Bluetooth Low Energy signals emitted from low-cost Bluetooth beacons allocated and distributed to the trucks visiting the port. SMATS data analytics cloud application, iNode, was used to process the sensor’s raw data, including beacon’s MAC addresses and signal strengths in real-time. Figure 1 illustrates different components of the SMATS technology used in this project.

    Figure 1: Illustration of SMATS’ truck traffic monitoring technology

    Bluetooth Beacon 

    The Bluetooth beacon cards used in this project were distributed to the trucks. These cards, illustrated in figure 2, can be attached to the existing RFID access cards or can be placed in the trucks. The card uses Bluetooth Low Energy (BLE) technology which are low-cost and provide multi-year battery life.

    Sensor Installation

    When deploying the sensors, the SMATS team checked the area to find the best locations for available mounting infrastructure and power sources. Based on the deployment locations, some sensors were powered by solar panels, and the others were connected to the available AC power. Figures 3 and 4 illustrate sensor installation locations.

    Figure 2- The Bluetooth Low Energy beacon card used to track truck locations

    Figure 3- TrafficXHub sensor installed at a product location

    Figure 4- TrafficXHub sensor installed at a gate

    The Results

    Advanced algorithms were used to locate the trucks at the Port and measure queue times between two sensors (a.k.a links travel time), queues between multiple sensors (a.k.a. route travel time), and idle times at the sensor location for goods pick up and drop off, illustrated in figure 5. In addition, an origin-destination analysis tool was built to track the movement patterns of trucks inside the Port, from the Port gates, to the scales, offices, product storage site, and exits, illustrated in figure 6. The SMATS hardware and software solution provided greater visibility to the Port’s truck activities and various performance measures for operational and long-term planning and decision-making.

    This information is expected to support the Port’s strategic (optimizing asset acquisition), tactical (optimizing use of existing assets), and operational (optimizing real-time activities) decisions to improve the performance of the Port and terminal services. iNode origin-destination analysis tools can reveal the truck movements in the Port and provide new valuable KPIs to decision-makers.

    In summary:

    • The sensors demonstrated reliable performance throughout the year
    • The Beacon cards were captured in the travel route by the sensors
    • iNode measures the travel time and wait-times with 92% accuracy
    • iNode shows traffic patterns and routes taken by trucks
    • iNode provides real-time locations of the trucks within the Port

    Figure 5- Real-time traffic data for configured links

    Figure 6- Movement data from a single truck and the Idle-Time (wait-time) and Travel Time traffic KPIs


    This project was funded by Innovation, Science and Economic Development Canada (ISED) and Transport Canada. We gratefully acknowledge ISED and Transport Canada for supporting and overseeing this project.



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