Experimental design and modelling

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1 Experimental design and modelling

2 Photo on the front: installation of the EVI beacons on the A12 motorway

3 Experimental design and modelling Spitsmijden Jasper Knockaert (ed.), Michiel Bliemer, Dick Ettema, Dusica Joksimovic, Albert Mulder, Jan Rouwendal, Dirk van Amelsfort

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5 1 INTRODUCTION The Dutch Spitsmijden 1 project was set up to study the feasibility of a reward scheme to encourage commuters not to drive during the morning rush-hour. The project comprises two stages. Stage 1 the reward trial reported on here comprised a behavioural analysis, technical and organizational implementation, welfare optimization and traffic simulation. The trial was carried out by a public-private partnership comprising universities, private companies and public institutions. The geographical focus was on the heavily congested Dutch A12 motorway link from Zoetermeer towards The Hague. An experiment involving 340 regular rush-hour commuters was conducted in order to obtain revealed preference observations for a behavioural analysis. This was complemented by several surveys (including a stated preference survey), which extended the scope of the dataset. The behavioural analysis led to the establishment of a number of parameters. These were fed into simulation models that had been developed as part of the project. One model is based on economic welfare theory and was used to determine the optimal reward level. The second model is a dynamic traffic model that allowed the simulation of different reward levels and an assessment of the global impact of the corresponding reward schemes. Three reports were published after the conclusion of stage 1: Effects of reward. This report (which is in Dutch) describes the most relevant results of the trial. A summary of this report is also available in English. Lessons learned. This report contains the lessons learned by the various individuals and organizations directly involved in the execution of the trial. (Available only in Dutch). Experimental design and modelling. This report provides an extended background description of the different technical and scientific aspects of the first phase of the project. (Available only in English). Stage 1 focused on preliminary behavioural analyses and the development of simulation tools. The results are presented in this report. Section 3 details the set-up of the reward trial and the corresponding behavioural analysis. Section 4 briefly describes the different surveys that were carried out. Sections 5 and 6 discuss traffic data and the design of the dynamic traffic model. The simulation results are presented for two scenarios in order to illustrate the model dynamics. Section 7 describes the design and calibration of the economic queuing model. Section 8 concludes this report. The research questions from the various surveys are listed in the appendices. A plan of approach has been drawn up for stage 2 of the project. In stage 1, the main focus was on conducting the trial and designing the simulation modelling framework, while in stage 2 the bulk of the behavioural analysis and the refinement of the simulation tools will be carried out. Further, a new trial may be performed in stage 2. We have also performed a transition study to find out how such projects as Spitsmijden might contribute towards a transition to sustainable mobility. 1 Spitsmijden can be roughly translated as avoiding rush hour. Spitsmijden Experimental design and modelling 3

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7 2 CONTENTS 1 introduction contents the spitsmijden reward trial Overview Reward management Technique used in the trial Quality control of data and processes Special traffic circumstances Analysis surveys network, travel and traffic data Network infrastructure description Travel demand Traffic data analyses with the indy traffic model Model description Model estimation and calibration Case studies Model results economic queuing model Introduction The bottleneck model The data and the model Application of the model Concluding remarks conclusions bibliography...79 appendices...82 Spitsmijden Experimental design and modelling 5

8 3 THE SPITSMIJDEN REWARD TRIAL 3.1 Overview This section discusses the organization of the Spitsmijden reward trial, including its location and timing, the rules of the trial, and the recruitment and characteristics of the participants. Location and timing The trial was launched on 2 October The test area was the Dutch A12 motorway corridor from Zoetermeer towards The Hague. On weekday mornings, this stretch of motorway is heavily congested with vehicles heading towards The Hague. There are few alternative routes or on- or off-ramps on this stretch of motorway, which made the trial relatively easy to control. The morning rush-hour was defined as lasting from to 09.30h, since this period has the highest traffic densities. The participants in the trial could earn a reward for not travelling by car from Zoetermeer to The Hague during the morning rush hour. Upon registration, the participants chose one of two types of reward. The first type of reward was an amount of money for each morning rush hour that the participant avoided. The second type comprised savings towards keeping the Yeti smartphone at the end of the trial. An extended specification of both reward types is provided in section 3.2. Rules The following were the main rules of the trial: The participants were to commute at least three times per week from Zoetermeer towards The Hague. They were to have access to and the Internet. They were to complete questionnaires and travel logs completely and timely. Their participation had to be voluntary (although they were required to sign a contract listing the rights and duties of both parties). They would receive a reward only for the number of times they avoided the morning rush-hour by travelling outside the rush hour period, using another mode of transport or working at home. The frequency of rush hour avoidance was determined relative to each participant s usual commuting behaviour (see 3.2; Rewards classes). The participants who were participating in the Yeti variant had to switch on the Yeti smartphone during each car trip. The participants would use the car in which an On Board Unit (OBU) had been installed. Recruitment of participants The objective was to recruit 500 participants. To this end, we organized three recruitment waves: Recruitment in April 2006 Licence plate recognition cameras were used to select 2,300 vehicles that travelled from Zoetermeer to The Hague at least three times per week. The Department of Road Transport (RDW) provided the names and addresses of the car owners. These people were approached by mail on 15 April 2006 with an invitation to participate in the trial. The sample of 2,300 cars comprised private vehicles as well as leased and company cars. Recruitment in June 2006 Using the same method, a second group of car owners were approached on 16 June All these cars were private vehicles. 6 Experimental design and modelling Spitsmijden

9 Recruitment in July and August 2006 The first two waves of invitations resulted in 283 participants. To increase this number, two further actions were launched, namely: Recruitment according to a member get member approach. Participants could register new participants via the Internet. Vehicle owners from the April and June waves who had not responded were approached again. In the end, 340 commuters participated in the trial. Participants Upon recruitment, the participants completed the first questionnaire about their daily commute (see D1 in Chapter 4), as well as a second one concerning their sociodemographic characteristics and organization of work and household (D2). After the trial, they filled out a third questionnaire regarding why they had participated and the experiences they had had with participation (D5). The following subsections provide a description of the participants based on these data collections. Of the participants, 64.7% were male. About half of all participants were aged between 35 and 49 (see Figure 3.1). About 25% were younger than 35, while 25% were older than 49. Figure 3.1: Participants ages leeftijdsverdeling deelnemers <25 years >49 years leeftijdsverdeling deelnemers years years <25 jaar jaar jaar >49 jaar <25 jaar jaar jaar >49 jaar The majority of the participants held a higher professional education certificate or a university degree. opleidingsniveau deelnemers Figure 3.2: Participants education levels opleidingsniveau deelnemers HBO / WO VMBO / HAVO LBO MBO VMBO/HAVO VMBO/HAVO LBO MBO HBO/WO Pre-vocational secondary education / senior general secondary education VMBO/HAVO LBO Lower vocational education LBO MBO HBO/WO Higher professional education / HBO/WO university education MBO Upper secondary vocational education Deelnemers naar huishoudenssamenstelling Alleenstaand Deelnemers naar huishoudenssamenstelling Getrouwd/samenwonenn zonder kinderen Getrouwd/samenwonend Alleenstaand met kinderen Alleenstaande Getrouwd/samenwonenn ouder zonder kinderen Overig Getrouwd/samenwonend met kinderen Alleenstaande ouder Spitsmijden Experimental design and modelling 7

10 Most of the participants were married or cohabiting; most had children (see Figure 3.3). Deelnemers naar huishoudenssamenstelling Figure 3.3: Participants household composition 5% 2% 13% Alleenstaand 56% 24% Getrouwd/samenwonenn zonder kinderen 13% Single Getrouwd/samenwonend 24% Living with partner without children met kinderen 56% Living with partner with children Alleenstaande ouder 5% Single parent Overig 2% Other Of the participants, 98% lived in Zoetermeer; the rest lived in the surrounding municipalities (e.g. Benthuizen, Berkel en Rodenrijs, Bleiswijk; see Table 3.1). Most of the participants worked in The Hague, although some worked in Delft, Leidschendam, Rijswijk or Voorburg. Frequentie woon-werk rit Table 3.1: Participants work location Work location Number Percentage The Hague % Delft 29 9% Leidschendam 10 3% Rijswijk 42 12% Voorburg 10 3% Other 57 17% Total % 3 keer/week 4 keer/week 5 of meer keer/week Motivations for participation The motivations for participation are presented in Table 3.2. The results suggest that the reward was the most frequent reason for participation, although the majority of the participants also had another motivation. Both the contribution to more insight into congestion and experimentation with alternative travel options were relevant motivations. Table 3.2: Participants motives Motive Number Percentage Reward (money or Yeti) % Contribute to understanding of road use during rush-hour % Contribute to reducing traffic problems % Experimenting with possibilities of adapting own behaviour % Gaining experience with the Yeti smartphone and the use of traffic information 9 2.5% Other % 8 Experimental design and modelling Spitsmijden

11 Overig The daily commute Of the participants, 62% commuted at least five times per week towards The Hague, using the A12 motorway; 26% commuted four times per week (Figure 3.4). Frequentie woon-werk rit Figure 3.4: Weekly commuting frequency 12% 62% 26% 3 keer/week 4 keer/week 12% 5 of 3 meer x keer/week 26% 4 x 62% 5 x or more 84% of the participants commuted only by car, including car trips both during and outside the rush-hour. The other 16% combined using their car with using a Park&Ride scheme (6.4% of the participants), motorbike (2.4%), train (4.7%), bus (1.8%) or bicycle (6.2%). 34% said that they regard public transport as a serious travel option, while 18% regarded cycling as an option. The average commute time (including congestion) was 36 minutes. The average reported free-flow time was 20 minutes, implying an average delay due to congestion of 16 minutes. The vast majority (90%) of the participants usually arrived at their workplace between and 09.30h (the morning rush-hour period as defined in this study) (Table 3.3). Table 3.3: Start of working day Start of working day Number Percentage Before 06.30h 6 2% h 15 4% h % h % After 09.30h 11 3% Unknown 1 0% Total % Organization of work and household Fifty-seven per cent of the participants were allowed to start work later than the usual start time (Table 3.4). Mostly, a delay of up to 60 minutes was allowed. Table 3.4: Possibilities to start work later Possibilities to start work later Number Percentage I cannot start work later % I can start work at most 30 minutes later 43 13% I can start work minutes later 86 25% I can start work minutes later 54 16% I can start work more than 120 minutes later 11 3% Total % Spitsmijden Experimental design and modelling 9

12 The majority (79%) of the participants could start work right away if they arrived early at their workplace (Table 3.5); 9% could start preparations. An early departure from home in order to avoid the rush-hour was therefore an option for many of the participants. Table 3.5: Situation when arriving at work early Situation Number Percentage I can begin work immediately % I cannot begin work, but I can begin making preparations for work 32 9% I must wait to begin work at a certain time (e.g. shift work) 11 3% I have to wait for colleagues before I can start work 7 2% I cannot enter the building / office 8 2% Other 14 4% Total % In addition to work flexibility, the flexibility of household activities played a role in the participants responses. In total, 54% faced constraints stemming from household obligations that prevented them from making an early or a late departure from home. Table 3.6 suggests that especially child care and dropping off children at school prohibited an early or a late departure. Table 3.6: Limitations on departure time to go to work Limitation Percentage Child care 29.4% Breakfast with family 9.7% Bring children to school 19.4% Drop partner off 5.9% Carpool appointments 2.4% Other appointments 10.9% Other 8.2% None 46.2% Use of travel information 41% of the participants said that they use road traffic information at least once a week. This percentage is the same for participants who chose the financial reward (40%) and for those who chose the Yeti smartphone (43%). Only 3% used traffic information regarding public transport once a week or more. 3.2 Reward management This section describes how the reward management framework was designed and implemented for the trial. We first discuss the two reward types, namely money and a Yeti smartphone. We then describe how different variations of each type were alternated in order to measure the behavioural response to different levels of the stimulous. Subsequently, we detail the assignment of the participants to reward classes, which reflected their travelling frequency under undisturbed circumstances. Finally, we discus the actual implementation of the reward management scheme and provide a brief note on how feedback to the participants was organized. 10 Experimental design and modelling Spitsmijden

13 Reward type Upon registration the participants were asked which type of reward they would prefer. There were two options. The first type of reward was an amount of money for each morning rush hour that the participant avoided. At the moment of registration the premium was indicated to amount to about 5. The second type of reward was saving for a Yeti smartphone. These participants received a Yeti smartphone at the beginning of the trial. The Yeti provided them with traffic information during the trial. If the number of avoided car trips during the morning rush hour exceeded a stated number, the participant would be allowed to keep the Yeti at the end of the trial. If the participant failed to meet the threshold, he / she would have to return the smartphone at the end of the trial. Thus, it was an all-or-nothing scenario. The majority of the participants chose a monetary reward. As the trial was set up to test both reward types, the remainder of the participants (including those who had said that they did not have a preference for one reward type over the other) were assigned to the Yeti variant. However, to prevent participants ending up with an unwanted and hence undervalued reward type, we allowed them to switch to the other type until the start of the trial. The final division of participants over reward type is provided in Table 3.7. Table 3.7: Classification of participants over reward types 2 Reward type Number Monetary reward 232 Yeti smartphone 108 Reward scheme In order to increase the amount of information regarding rush hour travel behaviour collected during the trial, the level of the reward proposed to each participant was varied over time. Money After installation of the measurement equipment (an OBU; see 3.3; EVI system) in all participants cars, a reward-free period of two weeks was scheduled. The participants were told that these would be test weeks in order to check that all the equipment worked as expected. They were also told that they should complete their daily logbook (see 3.3; Website and logbook) on the website, in order to test the full set-up of the trial. However, the real purpose of the test weeks (apart from technical testing) was to collect information on travel behaviour when no reward was provided. This served as a reference situation both for the behavioural analysis ( 3.6) and for the design of the reward management (see 3.2; Reward classes ). The former motivation is the main reason the participants had to complete their logbook during the test weeks, the latter argument is why we did not tell the participants what we were actually measuring, in order to avoid any bias in reference (unrewarded) behaviour. 2 It should be noted that the number of participants may vary over different sections of this report, depending on the selection of participants used in a particular section: for some participants, only partial information was collected and they may or may not have been included in selected tables or figures. Spitsmijden Experimental design and modelling 11

14 It should be noted that we did observe some change in behaviour in the test weeks. This was measured by comparing observations for the test weeks with the weeks before for those cars that had been equipped at the beginning of the installation period (which took two weeks). Feedback from the participants also indicated that some of them changed their behaviour during the test weeks. The main argument put forward by these participants was that they perceived the test week message as a call to see if they could change their behaviour. Also some participants made arrangements at their workplace before the trial. It is not possible to know how large this share of participants was, but it is our impression that it was small. For the reasons mentioned above, we decided to add another reward-free week at the end of the trial during which the participants would have to provide logbook information. This time we told the participants that they were expected to behave as they would normally behave without a reward. It was stressed that their rush-hour travel behaviour would in no way affect their reward. Nevertheless, we again got some feedback that some participants continued to avoid the rush-hour, but now the motivation was that since they had learned how to avoid it (motivated by the reward), the new situation pleased them more than they had expected and they had decided to continue their new behaviour even in the absence of a reward. During the ten-week period between the reward-free weeks, the participants could obtain a reward by avoiding the rush-hour. In order to maximize the behavioural information, three reward levels were tested for: 3 reward for avoiding the h period for three weeks; 7 reward for avoiding the h period for four weeks; 3 reward for avoiding the h period, increased to 7 if the full rushhour ( h) was avoided, for three weeks. The definition of the period h as the morning rush-hour 3 was based on observations on the stretch of the A12 motorway extending from Zoetermeer to The Hague (see 3.1; Location and timing). Although all the participants dealt with all three reward levels for the same number of weeks, the order of the three variants was shifted in order to compensate in the analysis for any order-related bias. Table 3.8 illustrates the resulting six reward schemes: R1: no reward; R2: 3 / day; R3: 7 / day; R4: 3 7 / day. Table 3.8: Reward schemes for monetary reward Scheme Week R1 R1 R3 R3 R3 R3 R2 R2 R2 R4 R4 R4 R1 2 R1 R1 R3 R3 R3 R3 R4 R4 R4 R2 R2 R2 R1 3 R1 R1 R2 R2 R2 R3 R3 R3 R3 R4 R4 R4 R1 4 R1 R1 R2 R2 R2 R4 R4 R4 R3 R3 R3 R3 R1 5 R1 R1 R4 R4 R4 R3 R3 R3 R3 R2 R2 R2 R1 6 R1 R1 R4 R4 R4 R2 R2 R2 R3 R3 R3 R3 R1 3 Local time is used throughout this report. 12 Experimental design and modelling Spitsmijden

15 The participants were divided over the six reward schemes such that all schemes had approximately the same number of participants. Special attention was paid to participants living in the same household: they were assigned to the same scheme in order to avoid behavioural complications, such as the switching of cars within the household in order to increase the reward. YETI The Yeti participants underwent a similar scheme of two reward-free weeks plus one reward-free week at the beginning and at the end of the trial, respectively. During the first two weeks, the participants did not have a Yeti, whereas in the last (reward-free) week of the trial they did have one. For the Yeti participants, the aim was not only to measure the impact of a reward but also to test for the impact of traffic information. We therefore decided to increase the number of reward-free weeks. This led to two reward levels during the ten-week period, namely: For a period of five weeks: avoiding enough rush-hours in order to be allowed to retain the Yeti and traffic information; For another period of five weeks: only receiving traffic information. The resulting schemes are illustrated in Table 3.9: R1: no traffic information, no reward; R2: traffic information, reward; R3: traffic information, no reward. Table 3.9: Reward schemes for Yeti reward Scheme Week R1 R1 R2 R2 R2 R2 R2 R3 R3 R3 R3 R3 R3 2 R1 R1 R3 R3 R3 R3 R3 R2 R2 R2 R2 R2 R3 The participants were assigned evenly to both schemes. Again, participants living in the same household were assigned to the same scheme. Thus, the impact of the reward could be measured by comparing the behaviour during the five reward weeks to that during the eight reward-free weeks. As for the impact of traffic information, the behaviour during the two reward-free weeks could be compared with the other weeks (traffic information had been provided at all times to the participants who had a Yeti smartphone), compensating of course for the impact of the reward. Communication The customized reward schemes were communicated to the participants via their personalized webpages, which were part of the Spitsmijden website (see 3.3; Website and logbook). We drew attention to the availability of this personalized information in an issue of the weekly newsletter that we sent to the participants. Reward classes The way the participants were recruited meant that not all participants commuted from Zoetermeer towards The Hague every day during the morning rush-hour (see 3.1; Rules). It would be unfair to reward part-time travellers for every day that they Spitsmijden Experimental design and modelling 13

16 avoided the rush-hour, considering that even under their unrewarded behaviour they would not travel during rush-hour on some days. We therefore designed a procedure to define the reference (unrewarded) travel behaviour of each participant. It should be noted, however, that this customization was not motivated by analytical arguments: to allow for the behavioural analysis it did not matter that participants received a reward for every day that they avoided the rush-hour. 4 Reference travel behaviour The reference (i.e. unrewarded) travel behaviour was based on the observations carried out during the two reward-free weeks at the beginning of the trial. Observations on Monday through Friday were taken into consideration. Days on which the participant indicated in the logbook that he / she was ill or on holiday were eliminated from the observations, as observations on these days did not reflect regular travel behaviour. For each respondent and each observation day we first checked the automated car observations in the h period. 5 If a movement was registered, we checked whether there was an observation in the h period. 6 If this was again the case, we decided that the participant had travelled during the morning rush-hour on that day. If no car registration was available for the h period, we checked the corresponding logbook entry for that participant (if available), because a technical failure might have resulted in missing observations. 7 If the participant indicated in the logbook that he / she had travelled in the h period in his / her own car or in another car, or somebody else had travelled in his / her car, we included the day in the morning rush-hour trip count. A reference travel behaviour indicator was defined as the ratio of the number of morning rush-hour trips to the number of observation days, corrected for holidays and illness. The procedure took into account only observations of the two reward-free weeks at the start of the trial. We do, however, have some information concerning the weeks before for some participants. Based on these automated observations we recalculated the travel frequency in a similar way, however without compensating for holidays or illness, considering that no logbook information was available for this period. Also the fact that most cars were not equipped with registration technology for the full period of these two weeks was not taken into account. The final travel frequency indicator was defined as the maximum value of the indicators for both periods. This value was then used to assign the participants to reward classes. Definition of reward classes Four reward classes were defined to which the participants were assigned based on their value on the travel frequency indicator (see Table 3.10). Based on the reward class, a correction was applied to the reward scheme in order to take into 4 Modest lump sum compensation is not expected to impact travel behaviour significantly. 5 We only consider observations of OBU-equipped cars. 6 For the automated observations we defined a tolerance of 5 minutes. 7 Note that it is in the interest of the participant that we overestimate rather than underestimate the reference travel frequency. 14 Experimental design and modelling Spitsmijden

17 account reference (i.e. unrewarded) travel behaviour during the morning rush-hour. Table 3.10: Reward classes and their impact on reward level Reward Reference rush-hour Monetary reward: Yeti reward: class travel frequency max. number threshold level of rewards (per week) (over five weeks) A [ 3.5,5.0] 5 15 B [ 2.5,3.5[ 4 20 C [ 1.0,2.5[ 2 23 D [ 0.0,1.0[ 1 25 For the participants who chose the monetary reward, the reward class defined the maximum number of rewards they could receive each week. The rationale was that under unrewarded circumstances (reference travel behaviour), some participants would not travel during the rush-hour five days a week. In the trial we wanted to reward participants only for the additional days on which they avoided the morning rush-hour. We therefore limited the number (n) of rewards a participant could receive per week, based on their reference travel behaviour. Technically, this meant that independent of the actual motivation for avoiding the morning rush-hour, the first 5-n days on which the participant was not registered were unrewarded, and that on all subsequent days on which the participant was not observed during morning rush-hour he / she received a reward. It sometimes happened that the travel behaviour of a participant did not affect his / her reward. Take, for instance, the case of a participant in reward class D who travelled during rush-hour on Monday. As a result, is impossible for this participant to collect any reward during the remainder of the week concerned. This situation where the marginal stimulus disappears happened towards the end of every week for a couple of the participants. For the participants with the Yeti smartphone, the class defined the threshold value for the number of days on which the participant could avoid the morning rush-hour. If the participant met or exceeded the threshold value, he / she would be able to keep the Yeti smartphone at the end of the trial. If the participant failed to meet the threshold value, he / she would have to return the phone, but not until the end of the trial. All days on which the participant was not registered in the morning rush-hour counted towards meeting the threshold; hence, this threshold value was larger for participants who travelled less in unrewarded circumstances. Manual corrections Some manual corrections on the assignment of participants to reward classes had to be applied. This was for several reasons. Firstly, because for some of the participants the number of irregular days (holiday or ill) during the two reward-free weeks was too large. Those participants for whom this number was seven or more were classified based on stated travel frequency (taken from the survey or the original registration D1 for the trial; D2 see Chapter 4). Secondly, because a customized message had been entered in the logbook for one or more days. These participants were classified manually in line with the philosophy of the automatic algorithm. Spitsmijden Experimental design and modelling 15

18 Thirdly, because some participants expressed their opposition to the classification. These complaints were processed manually by the project office. Different data were compared (observations as well as stated travel frequency) with the arguments put forward by the participant. Based on these considerations the reward classification was re-evaluated. For some participants this resulted in an upgrade (applied retroactively), which was subsequently communicated to the participant; for others, however, we judged that their classification was correct and that it was more likely that the complaint was related to the participant s misunderstanding of the reward class concept, in which case we answered the request by providing a more personalized explanation of the reward scheme dynamics. It is not surprising that most complaints were related to a perceived under-classification. Nevertheless, one participant argued that she should be assigned to a lower reward class. The dynamics of the trial sometimes resulted in a participant being reluctant to complete the logbook. In such cases, it could happen that upon a re-evaluation of the automatic classification a participant was assigned to a higher reward class as a result of updated logbook information. The upgrade was then assigned retroactively and communicated to the participant in a brief personal message. The final classification of the participants is shown in Table It can be seen that the bulk of the participants were assigned to class A or B (reference rush-hour travel frequency of 2.5 days a week or more). Less than 5% of the participants had a very low reference travel frequency (class D). Table 3.11: Classification of participants over reward classes Class Money Yeti Total A B C D Reward management The reward management was the algorithm used to calculate the reward that each participant received as a result of his / her participation in the trial. In a first step the daily observations including the logbook entry were translated into an indicator that had a value for each day and each participant. In a second step the actual reward was calculated, taking into account the reward class of the participant. For Yeti participants we compared in a final step the aggregated value of the weekly rewards with the reward class specific threshold value in order to determine if the participant could keep the Yeti at the end of the trial. Observations The observations were processed in the reward management algorithm following a procedure that is very similar to the one that was applied to assign participants to reward classes. For each respondent and each observation day we first checked the automated car observations in the h period. 8 If a movement was registered, we checked whether there was an observation in the h period. If this was again the case, we decided that the participant had travelled during the morning rush-hour on that day. 8 We only consider observations of OBU-equipped cars. 16 Experimental design and modelling Spitsmijden

19 If no car registration was available in the h period, we checked the corresponding logbook entry for that participant, because a technical failure may have resulted in missing observations. If a participant had indicated in his / her logbook that he / she travelled by car in the h period with his / her own car, or that somebody else travelled in the participant s car, we included the day in the morning rush-hour trip count. The output of this procedure is a variable p l,i,t,w which expresses the behaviour of participant i on day t (t=1...5) in week w (w=1...10) in the h period. 9 The variable has a value of 1 if the participant travelled in the period considered, and of 0 if he / she did not travel in the period considered. If there had been a manual evaluation (see further), the variable also has a value of 1 (i.e. the participant received a manually determined reward rather than the one determined by the algorithm). A similar approach was followed to determine if the participant travelled in the h period. The resulting variable is p s,i,t,w. Note that in all cases the unit of observation was the behaviour of the participant s car, rather than of the participant him- / herself. As a result, if somebody else travelled with the participant s car during rush-hour, the participant did not obtain a reward. Inversely, if the participant travelled in another car, he / she did receive a reward (even if he / she indicated this behaviour in the logbook). The rationale behind this rule is that through the OBU / EVI system (see 3.3; EVI system) we could fairly reliably register the use of the car, but not the identity of the driver. By making the reward dependent on car use only, we eliminated any motivation for fraudulent logbook completion, and hence improved the quality of information collected through the logbook. Separate procedures were put in place in order to check for drivers who reported too frequent use of other cars, or drivers who tampered with the OBU beacon. These procedures are discussed in section 3.4. A manually determined reward was necessary if a participant entered a customized message in the logbook. For the corresponding participants and days, the project office evaluated the available observations together with the comments and decided on the correct reward, which was then entered manually into the project database. Similarly, a manually evaluated reward was sometimes necessary in the case of a complaint formulated by a participant. Daily reward Based on the values of p l,i,t,w, p s,i,t,w and the manual reward r m,i,t,w, we calculated the reward on day t in week w as: r i,t,w = r m,i,t,w + r a,i,t,w - r a,i,t-1,w with: r a,i,0,w is zero by definition; r a,i,t,w the cumulative automatically determined reward on day t of week w. The cumulative automatically determined reward r a,i,t,w for participant i on day t of week (w) was defined as: 10 9 No bank holidays occurred during the trial, so all weeks (w) comprised five observation days. 10 The variable ra,i,t,w expresses the level at day t of the cumulative reward collected since the start of week w, without taking into account manual corrections (these are determined by r m,i,t,w ). Spitsmijden Experimental design and modelling 17

20 r a,i,t,w =max[0,(d i - ut p l,i,u,w )r l,i,w +(d i - ut p s,i,u,w )r s,i,w -(5-t)(r l,i,w +r s,i,w )] with: d i the maximum number of rewards participant i could receive per week (dependant on reward class of participant i, see Table 3.12); r l,i,w the reward participant i obtained by avoiding the rush-hour (see Table 3.13); r s,i,w the reward participant i obtained by avoiding the rush-hour (see Table 3.13). Table 3.12: Definition of parameter d i Reward class participant d i (monetary reward) d i (Yeti reward) A 5 5 B 4 5 C 2 5 D 1 5 Table 3.13: Definition of parameters r s,i,w and r l,i,w Reward level of participant i in week w Monetary reward Yeti reward (see reward schemes 3.2) r l,i,w r s,i,w r l,i,w r s,i,w R R R R4 4 3 For the participants, who received a monetary reward, the unit of the reward variable r i,t,w was the euro, whereas for participants who wanted to keep the Yeti smartphone, the reward variable r i,t,w had a value of 1 on days the participant managed to avoid the morning rush-hour and otherwise of zero. YETI As described above, each morning rush-hour that the Yeti participants avoided, counted. However, the threshold level to keep the Yeti at the end of the trial was differentiated according to the reward class the participant belonged to. This level was compared to w,t r i,t,w to evaluate if participant i had won his / her smartphone. Reward feedback Each participant s reward level was shown on that person s personalized webpage. For those who were reluctant to complete the logbook or fulfil other obligations (such as completing the surveys), we did not show their reward level. This was in order to motivate the participant to provide the information requested and (because the actual logbook entry could impact the reward level) in order to avoid changing historical reward levels or inducing tuning behaviour. The reward level was updated once a week. Every Friday, information up to the end of the previous week was processed in order to re-evaluate the cumulative reward. This prevented tuning behaviour (e.g. the five-minute tolerance was not formally communicated to the participants). 18 Experimental design and modelling Spitsmijden

21 3.3 Technique used in the trial In this part of the Spitsmijden project, an important role was given to the technical side of the trial, because without appropriate technical help the trial could not have been conducted. To support the trial, different techniques were applied for different purposes. The main idea was to apply and test existing and new traffic detection techniques. In addition, without a website it would not have been possible for the project office to function or for contact with the participants to be maintained. The Spitsmijden website was used for both the internal and the external communication. It was also necessary to design the logbooks such that they would provide useful information for the scientific analyses. All the participants were obliged to fill in these logbooks and to inform the project office about the travel decisions they made. Also the feasibility, correctness and practicability of different types of equipment for traffic detection and registration were tested. The advanced techniques used to detect traffic and to provide travel information to users comprised the following components: OBU devices; Camera systems; Yeti smartphone. Details about these traffic detection and recognition techniques are given further on in this document. In brief, the technical side of the project consisted of the following: Detection of car movements (OBU and cameras); Storing, filtering and accessing information about car movements, participants, bonuses, etc. (data structure and database); Providing information to the participants as well as to project groups, and communicating within the project and with the participants (website design); Collecting information about the participants travel decisions (logbooks); Providing the participants with traffic information (travel times on the A12 from Zoetermeer towards The Hague). All this is described in more detail in the following sections. EVI system An EVI (Electronic Vehicle Identification) system was implemented for the first time in the Netherlands in order to signal and register the participating vehicles. This section explains the technical design of and the results achieved by the EVI system. System components Broadly speaking, the system comprised four components. Together, they formed the complete chain from vehicle to registration (see Figure 3.5). Figure 3.5: EVI system components OBU EVI beacon Communication Back office OBU The participating vehicles were fitted with an On Board Unit (OBU) a small transponder / transmitter that was fixed in a holder attached to the windscreen. Each Spitsmijden Experimental design and modelling 19

22 OBU had a unique identifying code. As soon as a connection was made with an EVI beacon, the identifying information was transmitted. EVI beacon The EVI beacons were placed either on portals over the traffic lanes from which the readings were to be taken, or on posts situated next to the road (see Figure 3.6). One EVI beacon was used for each traffic lane. Each EVI beacon comprised an antenna and a registration unit. The antenna picked up the signals from the OBUs installed in the passing vehicles. Communication took place by means of DSRC (dedicated short range communication), using a radio frequency of 5.8 GHz. As soon as a vehicle was within range of the antenna, the information exchange between the OBU in the vehicle and the EVI beacon took place (see Figure 3.7). The information obtained was recorded in the registration unit. Figure 3.6: Single-lane EVI beacon Figure 3.7: Interaction between OBU and EVI beacon On Board Unit (OBU) Beacon The information registered by the unit was then downloaded to the back office for processing. Communication unit The communication unit provided the communication between the EVI beacons and the back-office system. For the trial, we chose a wireless system that uses GPRS / UMTS. It would of course have been possible to use a fixed network connection. Back-office system The back-office system comprised two components: EVI registration system: this component transferred information from the EVI beacons to the central system and registered it in the central EVI database; EVI management system: this component managed the various data collections in the registration, registration consultations and the supply of information from the registration. All the EVI readings were sent to the project office each day (see Figure 3.8). Figure 3.8: Management system 20 Experimental design and modelling Spitsmijden

23 EVI system data flow A detailed overview of the complete data stream of information in the EVI chain (i.e. from OBU to the project office) is presented in Figure Location overview For the trial, EVI beacons were placed on all main exit roads from Zoetermeer towards The Hague. The EVI beacons were situated at the following locations (see Figure 3.9): A12 (three lanes) Zwaardslootseweg (N206) Zoetermeerse rijbaan Katwijkerlaan Figure 3.9: Locations of the EVI beacons EVI Zwaardslootseweg (N206) EVI Zoetermeerse rijbaan EVI A12 EVI Katwijkerlaan A12 A multi-lane installation was placed over the A12. EVI beacons, linked together, were centrally located above each of the three lanes of the A12. Each beacon was connected to a single communication unit. Installation directly above the traffic lane at a height of five / six metres is the prescribed mounting for the guaranteed capture rate of 99.9%. Other main exit roads A single-lane installation was installed on the other main exit roads. These installations comprised an EVI beacon and a communication unit. For technical reasons and reasons related to planning permission, these single-lane beacons were sited adjacent to the road instead of overhead. Installation adjacent to the road puts higher demands on the tuning (aiming) of the beacon, which reduces the likelihood of good functioning. Test location In order to carry out test work, and for a managed installation of OBUs, a singlelane EVI beacon was installed in the RDW (Department of Road Transport) vehicle park in Zoetermeer. Spitsmijden Experimental design and modelling 21

24 Guarantee of correct functioning The data provided by the EVI system formed an important basis for the scientific study. The demonstrably correct functioning of the system was obviously of great importance. This was fully taken into account during the EVI set-up and preparation phase. The following measures were taken to guarantee the correct functioning of the EVI system. Proven technology A conscious choice was made to utilize proven technology. The technology used is currently being put to successful use in other countries (e.g. Austria and Portugal). Installation plus test and extension The fitting of the OBUs was carried out by qualified technical staff in accordance with the manufacturer s instructions and took place during special fitting evenings. Immediately following its installation, each OBU was tested to ensure that it was the correct OBU and was functioning properly. The beacon installed in the RDW parking facility was used for this. Tamper-proof The OBU holders were attached to windscreens using a special glue. If a holder was removed, the glue became visibly damaged and the holder could no longer be replaced. In combination with the fitting and removal by qualified staff, this eliminated the possibility of the temporary removal of the OBU without intervention by the project management. Tamper indicator Special tamper-proof OBUs were used. When an OBU was fitted to its holder, a switch was activated. If the OBU was then removed from its holder, the next time the vehicle passed an EVI beacon a signal ( OBU removed from holder ) was sent to the beacon. This was registered and the switch was reset for subsequent readings. This made it impossible for participants to remove the OBU from its holder unnoticed. System management A procedure was implemented to ensure the correct functioning of the EVI system. In the case of a breakdown, rapid intervention was possible. Installation test An EVI test location was set up at the RDW parking facility in Zoetermeer. This made it possible to simply and easily carry out advance control tests of the system and also to check the installation of the OBUs. Reserve beacon In order to be able to deal quickly with technical problems, a completely set-up and functioning reserve beacon was available throughout the trial. Thus, all components could have been replaced within 24 hours should the need have arisen. Cameras A licence plate recognition camera was installed at every EVI location to record the registration number of every vehicle that passed (see 3.3; Camera system). The camera compensated for any unsuccessful EVI registrations and thereby reduced the chance of missed registrations to nearly zero. This same technique is in use in Austria, where a camera is installed adjacent to the EVI beacon at every toll location. 22 Experimental design and modelling Spitsmijden

25 Facts and figures Participants The participants in the EVI part of the trial can be divided into two groups, namely those taking part in and those carrying out the trial (see Table 3.14). Table 3.14: EVI participant classification Type of participant Number Participant 344 Researcher 25 Readings More than 31,000 EVI readings were made during the measurement period. The readings were divided over the various locations as presented in Table Table 3.15: Readings at the different locations >49 jaar Location No. of readings A12 26,772 Zwaardslootseweg / Middelweg 1,392 Zoetermeerse rijbaan 1,463 RDW parking facility 1,266 leeftijdsverdeling deelnemers opleidingsniveau deelnemers Deelnemers naar huishoudenssamenstelling Katwijkerlaan / Pijnacker 677 <25 jaar VMBO/HAVO 2% 5% jaar RDW test / reserve beacon 15 LBO Total 31,585 13% jaar HBO/WO Spread across the morning rush-hour HBO 56% 24% The most important gauge for both the trial itself and the functioning of the EVI system was the multi-lane system installed over the A12. To illustrate this, the graph below (Figure 3.10) shows the distribution of all the EVI readings taken during the morning rush-hour (i.e h) throughout the measurement period (24 August January 2007). Frequentie woon-werk rit 12% Spread of EVI readings across rush hour Figure 3.10: Spread of EVI readings across the rush-hour A12 5,182 5,000 62% 26% 4,000 3,000 2,000 1, ,102 3,029 2,578 1,955 1,344 2, Number of EVI measurements per location 26,772 1,392 1, , % 99% 98% 97% 96% 95% OBU did not bleep The comparison between the observed and via camera system registered data For the duration of the trial, the participants kept a logbook (see 3.3; Website and Functioning logbook) per location in which the standard 500option No OBU beep could be ticked. This option was Manual count 400 Camera count Spitsmijden 300 Experimental design and modelling % 99.86% 98.91% 96.16% 100% 99.81% 200