Rolling Origin Destination Survey (RODS) data

RODS is an annual rolling survey programme which was launched in 1998. For this research, RODS is of interest to us because it reveals respondents’ selected path as well as their characteristics, which is vital for us to implement discrete choice model. Its main purpose for LU is to generate the OD matrix for daily underground services, and to estimate the flows between each OD pair. This is done by conducting passenger surveys at a random sample at selected underground stations (usually 30-40 stations subject to budget constraints) over continuous years. The selection of underground stations is determined by the expected changes in service provision and/or station ridership. During the survey, RODS questionnaires are randomly distributed to passengers who enter the station. The sample size is determined by using the hourly control totals for each underground station adjusted by the expected response rate. The assigned questionnaires are expected to be returned by mail. As shown in Appendix B, passengers are asked to provide detailed information on their journeys.

Specifically, the key questions include, but are not limited to:

 The origin and destination for this particular trip (address and postcode)

 Selected underground service

 Departure time of this journey

 All the other transport modes that are used in this journey

 Trip purpose

 Ticket type

 All underground stations used in this journey

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 Socio-demographic data including age, gender, travelling frequency and physical ability

This information is considered to be an essential input for several underground performance models. For instance, the Train Service Model applies RODS data as the key input for the analysis of demand. The Journey Time Metric (JTM) also uses RODS to calculate the weight of delay at a particular node in the whole journey. Moreover, the Pedroute Strategic Model (PEDS), which assesses the congestion and delay of underground lines, estimates flows at each entrance and exit on the basis of the passenger flows recorded in RODS.

While RODS represents a valuable data source to reveal route choice, we should be aware of several factors that make it less than perfect, and which have potential impacts on this research. Firstly, the sample size is still relatively small due to its limited sampling and low response rate (between 20% and 30% in recent years). This is the main obstacle for large-scale modelling since limited observations may lead to inaccurate estimation. Secondly, RODS is incapable of capturing the annual changes of passengers’ travel patterns since most stations are surveyed every 8-10 years. Consequently, some old samples cannot be employed since it is impossible to obtain corresponding train performance data (as explained in 7.4.3, performance data prior to 2006 cannot be retrieved). Last but not the least, RODS surveys are merely distributed to passengers from 7:00 to midnight on weekdays, and thus it does not record the actual travel pattern at weekends.

To address these weaknesses, we only look at weekday travel pattern, and attempt to enlarge sample size by incorporating more corridors. From the RODS data, we extracted a subsample of respondents who made a journey along one of the four corridors that we identified in Table 7.3. This original sample contained 702 passengers, but the final sample was reduced to 661 passengers after data-cleaning (missing data, and compatibility between RODS and the level-of-service dataset).⁴⁵ This current sample compares favourably to the SR91 sample, which has only 438 observations. We then split the sample into two for the purpose of calibration and prediction respectively. For this present analysis, a 75% subsample (497 observations) was used as a calibration sample, and the remainder (164 observations) retained for model validation.

The descriptive statistics of the calibration sample is shown in Table 7.4. In terms of subsamples on each study corridor, the most observations were collected from the Waterloo

45 The original observations of the RODS dataset (from 1998 to 2011) are much larger than the sample used in our final sample. However, we found out that many surveys were conducted before 2008, while the train performance data collected before 2008 is not available in the current TfL system (NetMIS data). Therefore, we had to abandon relatively old RODS data.

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station — Baker Street station (WB) scenario, with a total of 210 observations collected in 2006 and 2009. The King’s Cross St. Pancras station — Green Park station (KG) scenario also provides a relatively large sample with a total of 134 observations in 2008 and 2010, whilst the Finsbury Park station — King’s Cross St. Pancras station (FK) scenario merely contributes 67 observations collected in 2009. Finally, we also retrieved 86 more observations from the RODS dataset for our Finsbury Park station — Green Park station (FG) scenario, which was surveyed in 2009. The observed statistics of the chosen route favour the Jubilee line and Victoria line, with an overall sample proportion of 25% and 40%

respectively.

We are also interested in the segmentations of this population. This led to a subsample of 263 passengers travelling at peak hours, 234 passengers travelling at off-peak hours, 313 work trips, and 164 non-work trips. If we split the sample into two subsamples according to journey frequency, we find out that the proportion of frequent travellers varies across each choice scenario. Specifically, most respondents in the WB and FG samples reported that they normally have five or more trips per week, whilst only 38% and 39% respondents in the KG and FK samples, respectively, make frequent journeys using the particular underground service.

46 Peak hour is defined as between 7:00 and 10:00 by RODS. If we look at the demand trend from 2000 to 2010, however, we found out that passenger growth in travel before 7am is approximately 100% which is much more than the growth in the other periods (normally 20% growth). Given the considerable change of demand trend in the early morning period, we decided to define the morning peak period as 6:00-10:00.

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Table 7.4: Descriptive statistics of calibration sample

The RODS data presented in this section plays an essential role in this current research, given that it reveals passengers’ route choices and their socio-demographic information. It is expected that future research could employ an extra dataset to enlarge sample size, such as the London Underground Oyster Card data. For the current analysis, however, only the RODS dataset is adopted but we conclude that RODS is good enough to provide sufficient observations for calibration and validation. Based on this survey data, we can now proceed in the exploration of level-of-service data.