Traffic Count Analysis Using TII Data

National Traffic Trends

Figure 1.1 displays the total car traffic volumes for 2019 and 2022 on a national level. The graph shows that 2022 traffic count data remained slightly below 2019 (pre- COVID-19) figures for most weeks of the year.

Peak Traffic Patterns

Average national peak traffic time patterns are displayed in Figures 1.2 to 1.4. During the typical working week (Monday – Friday), morning traffic volumes peak at 8am, while in the evening traffic peaks at 5pm (see Figure 1.3). At the weekend a different traffic pattern is evident (see Figure 1.4) with a peak around 1pm.

Cross-border Traffic

The traffic count data was also used to analyse cross-border traffic patterns (see Figure 1.5). In this case the cross-border traffic data was used to create an index with January 2019 selected as a pre- COVID-19 base month. This allows for the comparison of monthly volumes during 2019 and 2022. 

The index shows that cross-border traffic volumes for 2022 were in general, lower than the corresponding month in 2019 with the exceptions of August, September, and November, when traffic volumes exceeded 2019 levels (see Figure 1.5). Indexing the traffic data also demonstrates the seasonal variation in cross-border traffic patterns as well as peak cross border traffic periods for differing years. For example, in 2019 cross-border travel peaked in July, but in 2022 this peak occurred later in the year, in August.

Cross-border traffic analysis has the potential to provide relevant statistics to inform policy in areas such as transport and tourism, and also provide a broad indication of cross-border economic activity.

Bus Traffic Activity and Tourism

A further aspect of this project was examining the usefulness of traffic count data as a potential indicator of tourist activity. This was accomplished by measuring bus traffic volumes close to selected tourist sites in 2019 and 2022 with the underlying assumption that some of this bus traffic would include tour buses (see Figures 1.6 to 1.8).

Analysing bus volumes close to popular tourist locations provides an indication of the level and patterns of tourist activity. The selected locations displayed a seasonal increase in bus volumes in the summer months. Interestingly, certain locations had different peak periods in terms of bus activity. The Cliffs of Moher had a peak in bus activity in June, with July being the month of peak bus volumes for the Rock of Cashel. Across other sites the pattern of bus activity was more variable with, for example, Newgrange experiencing peaks from May to July and again in October.

Using the traffic count data in this way has the potential to identify trends in tourist activity in key locations across the country. The traffic count data allows for a more in-depth analysis of tourism activity including regional seasonal trends. This offers the potential to link the findings with other data to identify correlations between visitor volumes and bus activity.

Conclusion

The findings of this project confirm the potential of traffic data to produce national and local trends in traffic activity and illustrates the potential of Big Data of this nature for official statistics.

The use of TII data through an API allows the CSO to publish preliminary estimates of traffic count statistics and trends within days of the end of the reference period. The nationwide coverage and 24/7 measurement of traffic activity in this research paper also highlights the potential of this data to inform new tourism and economic activity indicators.

This project did highlight some challenges in working with data of this nature and further work will be required to refine the methodology used. This is captured in more detail in the "Background Notes".