Modular Simulation-Based Physical and Emotional ... - IEEE Xplore

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Modular Simulation-Based Physical and Emotional Assessment of Ambient Intelligence in Traffic Andreas Riener, Matthew Fullerton, Christian Maag, Christian Mark, Cristina Beltran Ruiz, Juan Jesus Minguez Rubio, and Kashif Zia Abstract—In this letter, we detail a modular approach for measuring the secondary physical and emotional effects of ambient intelligence (AmI) technology in traffic. Using the case of merges on to a highway, we assess the results of a system that advises the driver to change early to a lane on the left to create space for merging cars downstream (tested using a cellular automata simulation). The indirect impact of the system downstream, namely how the remaining lane changes from the merge lane to the innermost lane proceed, is then evaluated using a time-discrete, space-continuous microscopic traffic simulation tool. This yields detailed results concerning driver interactions that can also be used to derive an estimate of driver anger in the situation. We have used real geographic, traffic and psychological data to test the system, and different models are used to accomplish various tasks. The approach yields (surprisingly) negative results concerning the indirect emotional impact of this AmI intervention which may be due to the nature of the lane changing model used and the chosen parameters. We argue that such an approach is also applicable to similar types of systems, where different data and model types are suited to different scenario elements. Index Terms—Adaptive systems, ambient intelligence (AmI), automotive applications, emergent phenomenon, human–computer interaction, intelligent control, mobile agents, optimization methods, simulation.

I. INTRODUCTION The potential for conflict between highway users arises from roadways being by their very nature shared spaces. One situation where the negotiation for space is crucial is merging (on-ramp) locations on highways. Avoiding conflict in this situation would ideally be an almost passive experience that happens as part of good driving, but it may also be a difficult, confusing process that leaves at least one party feeling wronged or having had their safety put in danger. Such an experience may likely also result in anger, and as negative emotions in traffic can increase the likelihood of reckless driving (e. g., tailgating and dangerous overtaking) [1], this emotional result may have further secondary effects later. In this letter, we present a method for measuring both physical highway-safety characteristics and inferring the emotional effect from

Manuscript received February 6, 2013; revised October 4, 2013 and December 13, 2013; accepted January 9, 2014. Date of publication February 19, 2014; date of current version March 12, 2014. This work was supported under the FP7 ICT Future Enabling Technologies Program of the European Commission under Grant 231288 (SOCIONICAL). A. Riener and M. Fullerton contributed equally to this work. This paper was recommended by Associate Editor M. C. Wright. A. Riener is with the Institute of Pervasive Computing, Johannes Kepler University, 4040 Linz, Austria (e-mail: [email protected]). M. Fullerton is with the Technische Universitaet Muenchen (TUM), 80333 Munich, Germany (e-mail: [email protected]). C. Maag and C. Mark are with the University Wuerzburg, Psychologisches Institut III, 97070 Wuerzburg, Germany (e-mail: maag@psychologie. uni-wuerzburg.de; [email protected]). C. B. Ruiz and J. J. M. Rubio are with the Sociedad Iberica de Construcciones Electricas (SICE), 28108 Alcobendas, Madrid, Spain (e-mail: [email protected]; [email protected]). K. Zia is with the Department of Computer Science, COMSATS Institute of Information Technology, University Road, Abbottabad, Pakistan (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/THMS.2014.2302389

those results at a merging point on a highway. The method uses a timediscrete, space-continuous microscopic traffic simulation tool to take measurements from driver interactions that can also be used to derive an estimate of the “affected” (innermost-lane) driver anger in the situation. We apply the method both to data measured on the Madrid M30 expressway ring (one of the most dynamic highway segments in Europe) and to the results of simulations that test ambient intelligence (AmI) technology upstream that tries to reduce the potential for conflicts between drivers who merge, and drivers who are already on the highway on this same stretch of roadway in Madrid. By AmI, we mean systems that are sensitive and responsive to environments, people and their behavior, delivering information to the driver that cannot be perceived without AmI. Hence, the systems both inform about the surrounding traffic area, especially other drivers’ intentions and weaknesses (perhaps also with recommendations), and are based on advanced systems with high interconnectivity [2]. This follows on from the previous work where the effects of the system in isolation were measured [3] and the application of an emotional analysis (based on data from driver-simulation experiments) to location unspecific simulation data without the AmI technology at work was demonstrated [2]. Outline The rest of the paper is structured as follows. Section II gives background information on the safety and emotional aspects of merging, including a summary of our previous work using driving simulator experiments [2]. The upstream AmI system [3] and the area from where our data are sourced in Madrid are also summarized. The results of the application of a more detailed simulation for time headway and anger measurement are then presented in Section III. Section IV provides a detailed discussion and interpretation of the results of this combined simulation study. Finally, Section V concludes the paper, summarizes the findings, and gives some recommendations for future studies. II. BACKGROUND A. Safety During Merging Maneuvers Our assessment is carried out in the shared area of the road where merging takes place. Here, a space for the merging driver often has to be established without a clear exchange of information or rules. The maneuver leads to quantitatively riskier behavior, shown by the fact that the frequency of short headways, considered more dangerous, increases with later merging, because when reaching the end of the onramp drivers feel pressed to perform the maneuver and accept smaller gaps [4]. The situation becomes more complex if drivers from other lanes are considered, e.g., vehicles traveling on the through lane of the highway change lanes in order to facilitate merging—a common phenomenon in highway traffic [5]. B. Anger Resulting From Merging Maneuvers A recent study on affective driving [6] reveals that anger is the third most important factor influencing driving performance (after fear and happiness driving). Anger thus demands particular attention for an emotion detection and regulation system in driving, and the literature discusses different reasons for anger while driving [7]. Personal and situational factors commonly influence driving behavior and could lead to aggressive driving [8], which is much more dangerous in terms of driving errors compared with fearful driving [9]. Particularly lanekeeping errors increase significantly for angry drivers. Anger is linked to physiological (increase in the heart rate, quicker breathing [10]), cognitive (different evaluation of situations), and behavioral symptoms

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Fig. 1. Driver on the innermost lane is confronted with two situations: a “normal” merging vehicle (upper row, C-THW = 0.8s), and a “rude” merging vehicle (second row, C-THW = 0.2s).

(aggressive actions). Nevertheless, physiological correlates of anger are often not specific, i. e., physiological parameters do not only correlate with anger but with many other emotional states such as, for example, fear [6]. Despite inherent subjectivity, self-reporting psychometric scales were constructed to measure anger [11] for this reason (as well as their easier implementation). In the previous work, we used a category sectioning scale [12] in merging interactions in driving simulator experiments [2]. The driver that drives on the innermost (right-hand) lane of the highway is confronted with a sudden change in time headway (THW; the new THW of the trailing, inner-lane vehicle after merging occurs is termed “created”-THW or C-THW) for two cases: a “normal” merging vehicle (C-THW = 0.8s) and a “rude” merging vehicle (C-THW = 0.2s) (see Fig. 1) and gives both a verbal category assignment (none, very little, little, medium, strong, or very strong) and numeric assignment (three levels for each verbal category apart from category “none”) according to how much anger the situation evoked. This leads to an anger rating between 0 and 15. The results showed that a normal lane change elicits little anger (mean 2.92); in contrast rude merging causes a significantly different anger value (mean 9.68, F (1, 9) = 32.54, p = .001). Further details on the experimental study can be found in [2]. C. Ambient Intelligence System to Improve Merging Measures for improving traffic are traditionally categorized as engineering, education, and enforcement. A new range of engineering approaches that try to improve the communication of intentions between drivers of different vehicles that interact with each other or the information flow between cars and the infrastructure is starting to emerge. In the previous work, we tested a driver warning about upcoming merging points ahead with the assumption that they then

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move out of the right-hand (innermost) lane if safe to do so [3]. This is based on the fact that it is obligatory to drive in this lane in most European countries. In fact, it is often used to capacity while other lanes still have room. The problem in merging areas is, however, that the entrance ramp also runs into this innermost lane. Although in areas without an on-ramp, drivers monopolizing the left (outermost) lane pose a problem, we found (using cellular automata simulations in NetLogo (http://ccl.northwestern.edu/netlogo) that a redirection of vehicles to spare capacity in the outer lanes on the left in the area approaching the on-ramp successfully harmonized traffic speed through the merging area [3]. For that simulation study and the new results in this paper, data from a single merging point on the Madrid expressway ring M30 were used. The central part of this segment is shown in Fig. 2. The main road has three lanes, where the lower (innermost) lane is used to let cars from the merging lane join. The lower road also has three lanes—the lower two are used by vehicles continuing, the upper (innermost) lane is taken by cars merging (via the merging lane) to the upper road. Moreover, the aerial photograph shows the traffic counters (induction loop sensors) embedded into each lane of the road, used for collecting data that drive the traffic simulations. Obviously, the system achieves a radical redistribution of traffic across lanes [3], and we found that this redistribution effect increases with the distance ahead that the information is given (perception range). This study result is shown in Fig. 3, where we have also added the “base case” of detector data without (simulated) AmI intervention. More details about the system and simulation study can be found in [3]. It is this result concerning changed lane flows that we use here to explore what knock-on effects this has at the merging point itself.

III. EXAMINING THE MERGING INTERACTIONS IN DETAIL The traffic flow effects of the system summarized in Section II-C lead to many situations where innermost lane traffic on the main road is reduced (see Fig. 3). Hence, we know that less merging interactions in this lane will take place, hopefully leading to a safer, smoother, and less stressful merging experience for many drivers. However, there may be additional benefits even for interactions where a path has to be “negotiated” on to the main carriageway with a driver on the innermost lane. Due to the reduction in traffic flow on this lane, we would expect the density of traffic to decrease and average C-THW to increase. To derive such detailed results, we now apply microscopic traffic simulation with a detailed model of driver merging behavior to the studied merging point and then further analyze the results using emotional data derived from driving-simulation experiments in a similar manner to [2]. To summarize, this approach involves fitting a function to the anger values resulting from the different experimental conditions, allowing anger values to be calculated for any value of C-THW. This allows us to test for physical (highway safety characteristics) and emotional effects of the system described in Section II-C arising indirectly. This analysis differs from that previously published in two key aspects. First, we previously used a cumulative normal distribution function; this form of function was shown to be suitable for describing anger resulting from driving situations in previous work, where much more data was available [13]. This fit, however, results in only limited anger even in situations approaching collision of C-THW= 0.0. Responding to criticism of this condition, we include here an extra point at [0.0, 1.0] in addition to the two points taken from the experiment results described in Section II-B. We use an exponential function that provides a good fit to the three points (see Fig. 4). The second key difference is that we analyze data derived from a real-road situation, using

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Fig. 2. Bird’s eye view of the central part of the implemented M30 segment. Innermost lane and entrance ramp are used by merging vehicles to change from the lower to main road, middle, and outermost lanes on the lower road are used by the cars continuing on that road.

Fig. 4. Values for THW are used to parameterize an exponential function for analysis in the traffic simulation. The anger is rescaled from 0. .15 to 0. .1.

Fig. 5. VISSIM network used for the simulation showing locations of relevant detector data (actual vehicle inputs are inserted further to the left).

Fig. 3. Main road traffic: Number of vehicles traveling in the innermost lane decreases with increasing perception range, while the number of vehicles remains almost constant in the middle lane. With increasing range of perception, vehicles tend to settle into the outermost lane (third row), making space on the innermost lane for merging vehicles.

a geometrically accurate road network and calibrated driver behavior for that network and traffic volume data from the no-AmI case. A. Microsimulation Methods We used the microscopic level traffic simulation VISSIM (http://vision-traffic.ptvgroup.com/en-uk/products/ptv-vissim/) to analyze the merging point (see Fig. 5). Traffic microsimulation pack-

ages (such as VISSIM) allow the collection of measures concerning the riskiness and efficiency of traffic flow considering all vehicle movements. The simulation allows a detailed examination of how vehicles react to one another’s movements (acceleration, THW, etc.), either posthoc or in real time. Vehicle movements are governed by a car following model, a lane-change model, and limiting vehicle acceleration/deceleration. Drivers make lane changes to reach their desired speed, or to follow an assigned route. The models of driver-vehicle dynamics are comparatively detailed compared with other models, allowing an analysis of traffic safety (e. g., [14]). Records of individual lane-change measurements are postprocessed from the lane changing record of the simulation. This provides the necessary source data for C-THW calculation, namely the displacement and speed of the vehicle that will be behind after the lane change takes place. Using the function shown in Fig. 4, anger values for all merges with interaction were then calculated for each of the three perception ranges.

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Fig. 6. Cumulative distribution of anger values (bin size = 0.01) for the three system variants and the no-AmI case. A curve approaching the upper-left more quickly indicates that a greater proportion of the values have a lower value along the anger scale. Surprisingly, anger values are increased in the merging interactions when AmI is present. These curves ignore the fact that the AmI system results in a lower number of merges overall (see Table I). TABLE I NUMBER OF ANGER-GENERATING INTERACTIONS, FAILED MERGES, FAILED VEHICLE INPUTS (VEHICLES THAT COULD NOT BE INSERTED INTO THE SIMULATION DUE TO CAPACITY CONSTRAINTS), LANE CHANGES INNERMOST LANE-MIDDLE LANE (2–3) AND LANE CHANGES MIDDLE LANE-OUTERMOST LANE (3–4) WITH INCREASING PERCEPTION RANGE

B. Results Counts from the simulation are shown in Table I. Due to decreasing traffic in the innermost lane, we see less anger-generating interactions (in this innermost lane) as perception range is increased. We also note fewer failed merges (merges that could not be implemented by the simulation model without >1.5 s of waiting at standstill) with increasing perception range. In addition (as to be expected), less lane-change maneuvers take place alongside the merging area (other drivers who ‘move over’). Results concerning anger in the interactions that do still take place are shown in Fig. 6. The strength of the anger in these remaining interactions shifts to slightly higher values for the AmI interventions, although not monotonically with perception range. As anger is monotonically increasing with C-THW, the changes in safety (based on C-THW) would be similar. IV. DISCUSSION The results of the analysis at the merging point show that C-THW decreases (i.e., maneuvers are more aggressive) despite there being less vehicles on the innermost lane. This leads to an increase in anger of the affected drivers on the innermost lane who have not moved to outer lanes. This is a surprising and unintuitive result: lower traffic volume implies more road space for drivers which we would expect to be used for more relaxed merges. From very limited data from the video of the merging area, we see this expectation fulfilled in real life; although smaller C-THW values are present in both high and low traffic scenarios, the values are suppressed in high traffic. We would caution that the finding in the simulation is probably a by-product of

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the aggressive behavior parameterization found necessary to achieve the required traffic capacity. It should also be noted that the values used for the analysis are those chosen by the simulated drivers at the beginning of the lane-change maneuver. Hence, it is also possible that in the AmI scenarios the drivers in the innermost lane would actually be able to reduce speed more easily due to less following vehicles in that lane. A more advanced analysis could attempt to extract the exact moment of merging interaction. Regarding the AmI system itself, it should be noted that its success depends a lot on the traffic situation. Large changes should be achievable in cases with light to medium traffic and a lot of unfamiliar drivers, while AmI intervention will not make such a difference at rush hour times (congested case) or for routine commuters who already know about merging areas in advance. We also ignore information on intended route/exit which might affect a driver’s willingness to change lanes. However, the system’s relative simplicity does not take away from the method’s ability to answer “what if” questions taking into account measured (real) traffic flow in a realistic environment, a hypothetical system (of any complexity), and drivers’ emotions. The success of any system will also depend on the driver’s response to the information and this will partly (depending on the system) be determined by how complex the information is and how it is presented. In related work, we have shown how a merging assistant with an enhanced reality component could look [15]. The fact that we obtain macroscopic results that are in line with the desired system goal but displeasing microscopic results downstream at the merge point itself is important: systems with primarily macroscopic design goals may have unwanted microscopic (e.g., safety, emotional) effects. However, it is just as important to caution that models that reflect reality at a macroscopic level do not necessarily accurately model the microscopic interactions even if these are being modeled in an explicitly microscopic fashion. Success at this level depends on the nature of the model and appropriate parameterization. Exact values of anger derived from the driving simulator results should not be used, as the situational parameters of the two simulations are not exactly matched. Seen from one perspective, the isolated situation examined in the driving simulator does not correspond exactly to the mixed interactions present in real traffic and traffic simulation. Or from the other, the situation of interest has not been fully isolated within the results of the traffic simulation. One difference between the driving simulator experiment and the traffic simulation is that merging vehicles may merge in front of vehicles that have themselves just merged onto the highway. It could be that the anger experienced in such a situation is different due to having just come from the merging driver’s perspective. It is also possible that the shape of the function is inappropriate, given the limited number of data points from the driving simulator experiment. Further experiments could examine multiple values and more complicated interactions; this can be facilitated through group driving in a driving simulator [15]. Alternatively or in addition, physiological correlations with anger could be established and measured in the driving situations. Nevertheless, the finding that anger increases with more dangerous behavior from the merging driver is unlikely to be changed meaning that the direction of the results would not change either. Obviously, the more accurate the model of emotion, the more accurate the precise emotional values will be: any form of function can be applied as long as the independent variable can be measured in the simulation. V. CONCLUSION Most drivers are aware from everyday driving that merging points give rise to fear, stress, anger, difficulty. In the best case, this is due to a misunderstanding that can be resolved between the drivers. In the worst

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case, accidents result as drivers’ mental capabilities are overstretched. As it would appear difficult to regulate such a high-speed situation better through driver education, the situation is one where we can consider communicative and sensing AmI technology, made possible by increased computational power and communication abilities, to hold much promise. Technology has already changed and will continue to change today’s traffic environment, and simulation is often turned to as a methodology for examining the effects of changes and technologies not yet implemented. Here, one exemplary AmI system that assists the driver before merging was examined using a combined modeling methodology. With this letter, we have shown how the offline coupling of three different forms of vehicle simulation allows the application of more or less complex simulation models and hence more or less parameters as deemed necessary according to the subquestion at hand. Simulation is also a suitable approach to perform optimizations in the model, allowing the transfer of optimized parameters back to the real world to adapt settings of AmI systems. The approach also allows the application of insights gained from traffic psychology to traffic engineering. The requirements on each study are relaxed by not having to simulate every aspect at once. For example, the “best-case” scenario of using all available space on the highway was tested rather than requiring real drivers to test the system or modeling a less-optimal response to the information. Instead, only an isolated part of the consequences was examined based on real driving experiments. This does not rule out testing the AmI system with real drivers later; on the contrary, the simulations would then allow us to scale up the results (e. g., reduced success rate in getting drivers to change lane early) of any such studies even if they were carried out with only a small number of drivers. This study demonstrates both the strengths and challenges of combining different simulation tools to simulate for varying levels of detail. In this example, the simplicity of NetLogo, a cellular automaton simulation, was used to directly move vehicles to lanes where space was available. It is in effect a “summary” or meso model of what is happening. At the merging point itself, where more detail on the exact distances and velocities was required during the maneuver, a microscopic car following and lane changing simulation tool, VISSIM, was used to generate the necessary measurements for further analysis. The results concerning anger can only be found from real drivers, and here a driving simulation environment was used that provides the emotional data. In such a driving simulation study, more than just accurate vehicle movements are required: in addition a realistic 3-D environment and feedback to the driver must also be provided. However, as discussed in Section IV, mixing different models requires clear isolation of matched parameters in order for each part’s results to be properly used. This was so far easily achievable for the NetLogo-VISSIM connection (traffic flows), but less straightforward for the application of the emotional function. If we went one step further and altered driver behavior in the microscopic simulation based on the emotional data in real time, even more careful relations between parameters would have to be established (e.g. [13]). In addition, we must concede that the study only examined a very isolated situation within a much greater system where there are other emotional effects (see Section II-B); only one merging point was considered, and there only the innermost lane (where driving simulator experiments showed that anger results from the interaction). Concerning the microscopic simulation, the merging behavior showed a need for either models that naturally exhibit diverse behavior, or better tools to manage diverse parameter sets for different driver types or driving conditions. [16] concluded that computational intelligence is the future of traffic signal control (TSC), but also stated that many questions are left. One interesting problem they identified and that may keep researchers away from these fields, are tedious pa-

rameter settings. In this case, a model that responds to overall traffic conditions with appropriate variations in aggressive merging would be particularly desirable. The differences between “normal” (no effect on the trailing, innermost-lane driver) and ‘forced’ (trailing driver forced to brake after merge) or “cooperative” (trailing driver anticipates merge and brakes to create space on highway) merges have been identified in real traffic and built into traffic simulation [17].

APPENDIX Although the aim was to assess traffic conditions with the AmI intervention, we do not have a data basis to validate vehicle movements against as the device has not been tested (in that scenario) in reality. In order to achieve reliable microscopic trajectories, we have to choose appropriate driver model parameters. Their suitability can be tested by running a simulation with no AmI intervention and comparing the results with the real data. A number of aspects of the simulation must be calibrated in order to achieve reliable results. Only one, aggressive mode of driver behavior was created, leading to short vehicle headways as observed in real data as well as replicating the traffic flows and speeds recorded at the expense of replicating the less aggressive (i. e., bigger headways) behavior also observed. Although the calibration cannot and should not guarantee an exact reflection of the trajectories that took place on the study day, we believe it ensures that the simulation is representative of real traffic with aggressive but successful maneuvers during congestion as observed in reality. We took the behavioral parameters emerging from this “no-AmI” scenario and applied them to the altered data resulting from the NetLogo simulation of the AmI system. A. Microscopic Simulation Calibration In the following, the calibration of the microscopic simulation system VISSIM is described. Where comparisons between source data and simulation results are made, data has been aggregated into 15-min intervals. Periods with five or more missing values were ignored for plotting and error calculation purposes. The measure of simulation accuracy used is root mean square percent error (RMSPE) [18]. For orientation purposes, Hourdakis et al. recommend < 15% for a good simulation [18], while German guidelines recommend < 5% [19]. A discussion of possible calibration strategies and the merits of several different measures can be found, for instance, in [20]. 1) Vehicle Speed: The speed of vehicles determines how fast each will reach a certain point and hence has a major influence on the merging interactions. Two desired driver-speed distributions (one for the merging vehicles, one for the straight-going vehicles) were created out of the “unhindered” vehicle speeds from 00:00–07:00 hours. 60 s interval data from traffic detectors at the site and the simulation for the innermost lane and merging lane (before and after the merging point for the innermost lane) is shown in Figs. 7–9. It is important to note both that correct speed cannot be maintained if flow breaks down in the simulation when it should not and also that the speed reduction observed in the simulation after 07:00 (t > 25200s) comes from limits caused by the traffic and not from a limiting, preset speed distribution. That said, we do not require speed breakdowns at exactly the same time or place as in reality, as such breakdowns can occur from particular car-to-car interactions that have a knock-on effect. 2) Lane Changing Choices: Broadly speaking, three types of lane change occur in the situation. Of direct concern are those from the merging lane to the main road. However, we need to also achieve an appropriate number of “moving over” lane changes from the innermost lane to the middle lane. Finally, many lane changes will occur simply

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Fig. 7. Speed along the real highway innermost lane before the merging point (PM41 in Fig. 5) compared with simulation. The RMSPE value is 7%.

Fig. 8. Speed along the real highway innermost lane after the merging point (PM81 in Fig. 5) compared with simulation. The RMSPE value is 10%.

Fig. 10. Comparison of lane-specific volumes after the merging point (PM81 in Fig. 5) from reality and the simulation—inner lane (above), middle lane (middle), outer lane (below). The RMSPE values are 40%, 31%, and 24%, respectively.

Fig. 9. Speed along the real merging road compared with simulation (PM45 in Fig. 5). The RMSPE value is 9%.

to improve the vehicle speed. In VISSIM, the first form of lane change was created by routing. Furthermore, a portion of the vehicles were routed such that they needed to stay on the innermost lane, to reduce unrealistic lane changes. The second-form of lane change in VISSIM can be modeled directly, in that vehicles cooperatively move over when they see another vehicle trying to change lanes, or indirectly, in that due to speed reduction at the merge point, the vehicles move to the faster lane to avoid the slow-down. We have used the second method in this case. In addition, lane changes from the middle to outermost lane were prevented. No lane changes are allowed on the approach road (which

is used purely as an input of lane-by-lane vehicles from the real data). The results on lane volume distribution for the detectors at the bottom of the simulation network are shown in Fig. 10. 3) Lane Changing Maneuvers: Here, not only the macroscopic outcome (e.g., lane volumes or speed considered above) is important but also the detailed car-to-car interactions. The car-following and lanechanging models were parameterized by examining speed-spacing data from a similar point on the M30 and from videos of the actual merging point for varying degrees of traffic. Here, it was observed that merging vehicles rarely come to a complete stop even during heavy traffic, although long queues of cars do emerge. When vehicles stopped (observed only once), it took around 1.5 s for the queue to start moving again. This value was used to set the “diffusion time” in VISSIM, whereby vehicles not having merged after this time are discarded as modeling errors. For this study, we especially wanted to prevent “stop

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Fig. 11. Comparison of spacing (gaps) versus velocity from real road data and simulation.

Fig. 12. Binning of time headways from same location and in the simulation. A peak arises in the simulation results because of the lack of diversity of driver types.

and go” merging, as it could lead to distorted results in the C-THW. The large number of other parameters that reflect both how safety distances will be reduced according to the length of road left for merging, and how cooperative drivers already on the highway are, were parameterized by eye. The main criteria (again, based on qualitative observations from video) were that drivers merge at different points (not only at the end of the merging lane) and at speed. As a partial validation, car-to-car spacings and time headways were used to test the spacings and time headways appearing in the simulation. These comparisons are shown in Figs. 11 and 12. ACKNOWLEDGMENT The authors would like to thank Madrid City Council for providing the traffic data. REFERENCES [1] SWOV, “Negative emotions and aggression in traffic,” SWOV Institute for Road Safety Research, Leidschendam, the Netherlands, Fact sheet, Jan. 24, 2011, p. 6.

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