If you’ve ever lost your phone only to find it shortly after, you probably used an app tracker to help you find it. While that’s amazing technology in itself, it only answers the immediate question of where you lost your phone. Now imagine it could also tell you why and how you lost your phone in the first place to avoid losing it again.
GPS and other tracking software have become commonplace in many transportation organizations to help establish timelines and pinpoint where assets are located. The only problem is that it lacks context to better understand why situations are arising and how to better plan for them in the future. For transportation planners, it is crucial to go beyond just the ‘where’ and ‘when’ of traffic to dive deeper into the ‘why’ and ‘how’ for a more holistic view of local and regional road usage.
Standard methods miss the mark
Traditional transportation data capture processes like mail-in surveys tried to address the lack of detailed trip context necessary, but low response rates kept these surveys from being truly impactful. As technologies evolved, automated vehicle detection systems were put in place to capture more traffic data but somewhere along the way, the granular context behind the metrics they produced was lost. This made it extremely difficult to understand any external factors for road usage or to spot patterns and traffic anomalies. However, it has become increasingly necessary to dive deeper as the key to more informed transportation planning decisions lies in the context.
Consider how in the past departments of transportation prioritized roadways for heavy-duty trucks when it came to moving freight. Now as we’ve seen post-Covid, the landscape for commercial transport and last mile delivery has changed so dramatically that not all vehicles used in moving goods are heavy-duty. Building out transport infrastructure is an extremely costly and time-sensitive exercise, and without the knowledge that a significant percentage of commercial transport is now made up of lighter-duty vehicles, planners might have continued focusing on prioritizing roadways for heavy-duty transport to and from their port. It is this kind of context and deeper insight into transportation behavior and patterns that will better shape infrastructure planning in the future.
Transportation data deep-dive
Knowing exactly what kind of freight, the purpose of the freight and how it moves and interacts within a city is the biggest challenge facing most transportation planners. These insights are a crucial component to completing the full transportation story of a region. Gaining visibility into the types of industries on the road is perhaps the first step into realizing how road networks are being used by different classes of vehicles. Understanding which industries are the largest contributors to road volume can help with incentive programs for certain key industries.
In addition to industry, connecting vocation context to this scenario would be a real game-changer and allow transportation planners to understand the purpose of those vehicles’ trips. Perhaps there’s a heavy-duty vehicle traveling from hub to hub with truckloads of merchandise or it could be a light-duty vehicle taking goods from the hub to the doorstep of a consumer. Although these vehicles are both in the retail industry, their purpose for using the road network will be completely different.
Vehicle vocations can change drastically from region to region as well, even in the same state. For example, the State of Texas might see more instances of long-distance vehicles attributed to the oil and gas industry, whereas the City of Austin might see more instances of door-to-door vehicles attributed to retail. Without this level of insight into the vocations of vehicles in small and large regions, it becomes a guessing game as to where to begin planning and developing new infrastructure.
Intelligent transportation systems that combine data science and Artificial Intelligence (AI) with trip context and traffic volume will transform the way that planners make decisions. All of a sudden what was simply connected-vehicle data is now contextual people and goods movement insight that can be used to understand the ‘why’ behind existing and future transportation networks.
Discover how Geotab ITS is completing the picture for intelligent transportation at its.geotab.com.1
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