Improving the evidence base on journey time reliability on the Trunk Road Network in Scotland

Executive Summary

This Final Report is for the research project "Improving the evidence base on journey time reliability on the trunk road network in Scotland." awarded by Transport Scotland to the Institute for Transport Studies, University of Leeds in November 2013. The views expressed in this report are those of the authors and should not be taken to represent the views of Transport Scotland.

Main Findings

  • Views of companies, both involved in freight movement and not, were obtained via surveys.
  • Trunk road reliability in Scotland was perceived to have deteriorated in recent years, but this did not appear to be a matter of great concern to most companies responding.
  • Analysis of Automatic Traffic data Collection figures showed a range of journey time variability by month and detection site, but generally the journey time variability did not seem particularly great.
  • An approximate linear relationship was found between the standard deviation of average speeds, for days taken at monthly intervals, and the inverse of speed. Such a relationship might prove useful in forecasting journey time variability.

Survey Results

The study looked at the concept of Journey Time Reliability (JTR) and its valuation. Surveys were conducted of Scottish businesses, both of Freight Users (F), sample size 33, and those not involved with freight (NF), sample size 116. In broad terms, the perception was that JTR had got worse on the Scottish Trunk Road network in recent years, but that this had not yet become a pressing matter for the vast majority of respondents.

For the F survey, respondents views regarding whether travel times on the Scottish Trunk Road Network had become less or more reliable over the last 5 years are reported in Table 1. The majority indicated that the reliability of travel times has become worse, though that result may be biased somewhat if those who felt that were more likely to participate in our survey.

Table 1 Perceived Change in the Reliability of Travel Times on the Scottish Trunk Road Network in the last 5 Years (F Survey)
Less reliable 19 58%
The same 7 21%
More reliable 6 18%
Don't know 1 3%
TOTAL 33 100%

For the NF survey, Table 2 disaggregates by the different sectors of the economy. The Tourism/Ferries sector reports no deterioration in reliability. For Manufacturing, also, the percentage saying more reliable outweighs those saying less reliable. The Financial sector, on the other hand, felt that reliability has worsened.

Table 2 Perceived Change in Reliability of Travel Times on the Scottish Trunk Road Network in the Last 5 Years by Sector (NF Survey)
D – Digital etc 38% 38% 25% 0% 8
E – Energy/Forestry 56% 22% 22% 0% 9
F – Financial 88% 13% 0% 0% 8
R – Retail/Food/Drink 28% 44% 11% 17% 18
L – Life Sciences etc 42% 58% 0% 0% 12
T – Tourism/Ferries 0% 75% 25% 0% 8
C – Construction 50% 50% 0% 0% 8
M – Manufacturing 25% 25% 33% 17% 12
S – Services 52% 30% 17% 0% 23
G – Government 30% 60% 10% 0% 10
TOTAL RESPONSES 47 47 17 5 116
% 41% 41% 15% 4%

Table 3 reports responses regarding actual impacts on companies of trunk road unreliability in the last year.

Table 3 Reliability Impacts over the last year (NF Survey)

Reduced Productivity/Sales 86 28 2
Additional Transport Costs 75 38 3
Delays to time-critical Deliveries 86 27 3
Additional Staff Costs 81 33 2
Difficulty in Attracting Customers 100 12 4
Difficulties with Staff Travel (on business, and commuting) 46 69 1

Table 4 provides detail on why firms would like to see Scottish trunk road reliability improved. Respondents were asked to choose the 5 most important impacts, from the list of 9 shown in the table, and rank them 1 (for most important) to 5 (for least important).

Table 4 Perceived most important reasons to improve reliability, ranked here from 1 for most important down to 9 for least. (NF Survey)
Access to Customers 1
Productivity 2
Transport Costs 3
Attractiveness of area 4
Staff Recruitment 5
Access to Suppliers 6
Links between firm's locations 7
Business Confidence 8
Exports 9

As can be seen from Table 5, the chief causes of unreliability were felt to be the concentration of traffic at particular times and roads being insufficiently large to cope. Road works came in third place, with the remainder only being awarded "half marks". By location, road design received the highest rating in the Inverness (IN) area. That area, ignoring 'Others' (OT, ie. outside Scotland), also gave the highest rating to road size, with Glasgow (GL) giving it the lowest rating. Concentration of traffic at particular times was felt to be particularly impactful in the Aberdeen (AB) area, but not a great worry in the South West (AY) and the Inverness area. Road works were particularly blamed in Edinburgh (ED), Tayside (TY) and 'Others'. The weather did not appear to have been much of a problem in terms of unreliability, the previous 12 months having been unusually clement in Scotland. Accidents were felt to have particular impact in the Inverness area, but received in Edinburgh the lowest rating in the whole table. The Central (CE) area was usually in the middle of the pack.

Table 5 Ratings (out of 10, with greater impacts given higher ratings) of the Scale of Various Impacts on Unreliability, by Location. (NF Survey)
Road Design 5.5 4.1 4.9 4.7 4.3 6.6 4.2 6.5 5.2
Roads not large enough 7.6 6.0 7.1 7.0 5.6 8.1 6.8 8.8 7.1
Traffic concentrated at particular times 8.4 5.6 7.6 7.5 7.0 5.4 7.7 7.3 7.1
Road works 6.2 5.9 6.1 6.8 5.6 6.4 6.7 7.4 6.4
Weather 4.8 5.1 5.1 4.7 4.6 5.3 5.0 4.6 4.8
Accidents 4.5 4.4 5.1 4.0 5.1 6.7 5.2 4.8 4.9

The study looked closely at the topics of JTR and its valuation, both in the literature and in STAG. The latter seemed well up to date with the state of the art, and no major revisions were suggested. Both in STAG and in the literature, the accepted way of encapsulating the value of JTR changes is via the measure known as the Reliability Ratio (RR). For private road vehicles RR is defined as:

Reliability Ratio = (Value of ΔT) / (Value of ΔT) = VOR / VOT

where: VOR: value of reliability; VOT: value of travel time; ΔT : a change in the standard deviation of travel time; ΔT: an identical change in scheduled travel time.

The study has found the best estimate of RR for Freight to be 0.48, and for Non-Freight, say 'cars', to be 0.3. As the sample sizes were small, and the estimation uncertain, the recommended values more generally reflect values found in the recent international literature.

The recommendation of this report is that the following values be mentioned in STAG:

CAR JOURNEYS: Recommended value RR=0.8. If a sensitivity test value is required, take RR=0.4.

PUBLIC TRANSPORT: Not studied in this project. Note that for RAIL the ATOC (2002) range is RR=0.6 to RR=1.5. Note that the Expert Workshop of 2004 recommended RR=1.4. Note that recent is suggesting splitting by journey purpose, with RR=1 for BUSINESS and RR=0.6 for other modes. This project finds little ground for recommending different values for PT than for CARS, ie RR=0.8, with a sensitivity test alternative of RR=0.4.

FREIGHT: From the values available in the literature, supplemented to a limited extent by the results from the present study, the recommended best single value is RR=0.6, but with such uncertainty that a sensitivity test range of RR=0.4 to RR=0.8 is strongly advised.

Automatic Traffic data Collection

The study also looked at the data currently available that could be used to gauge Journey Time Reliability on the Scottish Trunk Road Network. The most useful source of data was found to be from Automatic Traffic data Collection sites, some of which recorded Vehicle By Vehicle data, showing vehicle type and speed. Should resources be available, this data could be converted to a common coding and aggregated in various ways. The study was able to mount a pilot investigation. Amongst other interesting findings, it was found possible to establish an approximate linear relationship between the standard deviation of average speeds, for days taken at monthly intervals, and the inverse of speed. It appears that around half of the variation in that standard deviation can be explained in that way. Since speed is routinely predicted, this opens the way to building a forecasting model of the standard deviation of speeds. Future appraisals might then be able to estimate both the mean and standard deviation of speeds on links affected by a scheme.