In most logistics scheduling systems, the navigation module primarily provides a distance matrix for backend algorithms to compute optimal or near-optimal routes. However, when the distance matrix fails to reflect real-world traffic conditions, the optimization results often hold only in mathematical models and struggle to be implemented in practice. Traffic speed fluctuations and road closures are among the key factors driving this gap.
1. Bridging the Gap Between Static Distances and Actual Traffic
In practice, route optimization often relies on static distance matrices, whose values are typically based on historical average speeds or simplified road weights. This approach implicitly assumes that traffic conditions are relatively stable. In reality, however, traffic is highly dependent on time, location, and unexpected events.
When road speeds drop significantly, or certain segments are temporarily closed due to accidents or construction, the distance matrix remains unchanged. As a result, optimization algorithms may select routes that are no longer reasonable—or even feasible—in the real world.
This situation not only leads to significant discrepancies between planned and actual travel times, but also causes users to question the value of “optimization” itself.
When a system consistently produces results that deviate greatly from reality, user trust in the model gradually erodes. Once trust is broken, it becomes extremely difficult to rebuild, even if the results improve later.
2. The Impact of Dynamic Input Rates on Algorithmic Complexity
If we attempt to incorporate dynamic input rates into the optimization process, the distance matrix is no longer a fixed input; instead, it becomes a time-dependent variable that changes continuously. This transforms the optimization problem from a relatively stable, static model into a highly dynamic and unpredictable decision-making challenge.
In such scenarios, the distance matrix may need to be updated frequently, and it can even yield different results depending on the sequence of routes. This significantly increases the computational workload. For the system, this translates into more route queries, larger matrix dimensions, and longer optimization computation times.
For users, the most direct consequence of this increased cost is longer waiting times for route results. In some cases, the system may be unable to provide real-time responses, which can negatively impact the overall user experience.
3. Accessibility Challenges Caused by Road Closures
Compared to changes in input rates, road closures have a more fundamental impact on route optimization because they directly alter the accessibility structure of the road network. When certain roads are completely impassable at specific times, the node relationships that previously existed in the distance matrix may no longer correspond to reality.
When considering routes between any two points, the need to check whether a path passes through a closed road effectively adds an additional feasibility check to every route calculation. This not only increases the computational burden but also makes the entire optimization process more difficult to predict and analyze.
The challenge becomes even more pronounced when road closures have time windows or directional restrictions. Whether a road is passable depends on the actual arrival time, which in turn is affected by the choice of preceding routes. This introduces a high degree of dependency and nonlinearity into the problem.
4. The Fundamental Trade-Off Between Traffic Conditions and Efficiency
When optimizing routes, incorporating real-time traffic conditions is essentially a trade-off in efficiency. The more detailed and up-to-date traffic information the model considers, the closer it aligns with reality—but the computational cost and system load also increase. Conversely, ignoring traffic conditions allows for faster results, but it may come at the expense of actual usability.
This trade-off is not purely a technical issue; it directly affects user experience and system positioning. Responses that are too slow can reduce user engagement, while overly idealized results may undermine trust in the system.
Therefore, the extent to which real-time traffic conditions are incorporated effectively determines whether a vehicle dispatch optimization system aims for a “mathematical optimum” or a “practically acceptable solution,” and there is often an inherent tension between these two goals.