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Exploring Relationships Between Traffic Volumes and Travel Delays


Mobility improvement has been on the list of everybody’s hot topics for a few decades now, and it continues to be critical for most cities globally. Various attempts to solve this challenging issue were endeavored, with less than satisfactory result in most cases.


When traffic congestion approached an intolerable level, many municipalities tried to limit the number of traffic participants in a variety of ways - from expansion of public transportation options to bans on entry into the central part of the city for certain cars on certain days. None of these worked to achieve much of improvement in traffic congestion, while causing a whole lot of significant, and obvious to all unpleasantries.


Unexpectedly and for the first time in history, COVID pandemic made it possible to objectively assess how traffic demand decrease impacts overall mobility. Indeed, two years of COVID pandemic with its stay-at-home orders naturally led to decrease in traffic demand with later return to initial numbers and above - while the traffic control did not experience any changes.


From the analysis of 196,891,570 data points on 126 road sections in nine Eastern US States, Ticon concluded that in general, the delays are impacted primarily by the quality of traffic management and not depends on traffic volumes that much. Fig. 1 shows that there is no exact correlation between traffic demand (volume) and travel delay.












Figure 1


In other words, same decrease of traffic volume may lead to extremely different changes in travel delay in different locations. Particular comparison of travel delay values on the same street at the same time in normal and COVID (54% travel demand decrease) circumstances is shown of fig.2. It is visible that the traffic delay did not change significantly, which is demonstrated by close colocation of pink and green lines.













Figure 2.


This means that the general strategy for restriction of traffic commonly adopted today is incorrect, and restrictive measures should be implemented only after it is objectively established that the relevant municipal departments achieved sufficiently high quality of traffic lights management.

More examples and statistics data in support of this statement are available in our case study “Impact of Traffic Volume Variations on Travel Delays as Illustrated by Pandemic Period Data”.


This study indicates that the traffic control advance, including but not limited to signal optimization, is much more effective tool for mobility improvement than traffic restriction measures. It also demonstrates that the change of drivers’ behavior caused by traffic demand fluctuations largely depends on the local conditions. As such, it cannot be predicted on the basis of averaged indicators (VMT and others) calculated on the level of state, or even of a county.


This is why each location of interest should be researched individually, with use of the latest available data.

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