5.2 Survey preparation and realisation
5.2.2 Assessment of survey techniques and development of evaluation methodology
After a rough pre-assessment of possible survey techniques according to the requirements described before, the following technologies have been further scrutinised:
• light barrier
• optical one-side sensor
• radar
• laser
• pressure sensor
• video
The requirements, namely high accuracy, measurement on intersection approaches, measurement inside of the intersections, economic viability, and simultaneous measurements at different locations, exclude many of the possible technologies.
The most feasible option is using video technology, because several parameters can be efficiently ob-tained. With a suitable viewing angle, the whole intersection can be captured at once. Because auto-matic detection methods so far have either too high requirements on the camera position and picture quality, or do not deliver the desired accuracy, the most viable option is a manual evaluation of the videos.
Drawbacks of video observations are insufficient resolution and distortion of the images. They impinge primarily on distance measurements and, because speeds can only be derived from a combination of time and distance measurements, on speed estimations. It was, therefore, decided to gather additional speed data with a more reliable technology. The most accurate and flexible measurements are possible using laser technology. Single vehicles can precisely be aimed at. Frequent measurements are possible, to analyse acceleration behaviour. The only constraint being the movement direction towards (or away from) the laser device to avoid systematic measurement errors.
5.2.2.2 Video observations
Two video observation techniques have been used:
• observation from bird’s view
• stop line observations
Deployed devices for bird’s view observations
The videos for the bird’s view observations were recorded using two IP cameras10mounted on a mobile mast approximately 25 m high. The cameras were connected to a computer, thus enabling panning and tilting with direct monitoring of the viewing angle. The videos were recorded in MJPEG format in Ad-vanced Systems Format (ASF) and Audio Video Interleave (AVI) containers respectively. The resolution of the cameras was set to CIF and VGA resolution respectively (i.e. 352x288 pixels and 640x480 pixels respectively). The frame rate averaged 25 to 30 frames per second. The cameras and the trailer with the extension mast can be seen in Figure 18 and Figure 19 on the next page.
Figure 18:IP Cameras used for video observations (mounted on extension mast)
The two cameras were equipped with wide angle lenses. Depending on the size of the intersection and the position of the mast, at least three approaches and the conflict areas in the intersection could be observed simultaneously. A single frame of a camera illustrating the viewing angle is given in Figure 20 on page 77.
Since the trailer, on which the mast was mounted, requires about 15-20 m2 area, the choice of the intersections was constricted. The trailer could only be placed on sufficiently wide walkways or adjacent parking sites.
Stop line observations
In addition to these high angle observations, stop line observations were conducted using a normal video camera with hard disc recording11. The videos are coded in MPEG-2 in High Definition resolution. The camera was placed near the stop line nearly perpendicular to the observed lane. In this way the stop line, the signal heads, and the vehicles could be observed at the same time. These observations were used to analyse start-up lost times and crossing times.
10 AXIS PTZ 215 and TRENDnet TV-IP410
11 Panasonic SDR-H280
5.2 Survey preparation and realisation 75
Figure 19:Extension mast located next to an intersection
Video evaluation
To get the data, the videos were manually processed using a subtitle generation software12. In the subtitles the crossing times of all vehicles at the stop line, the time at the conflict points, the vehicle type, the observed and conflicting stream, and special occurences were recorded. Each lane was evaluated separately.
The subtitles were recorded with a precision of a tenth of a second. The generated subtitle text files can easily be imported into other software, thus enabling the automated processing. Visual Basic modules were programmed to transfer the subtitles into MS Excel and evaluate the contained information. Invalid measurements due to missing frames were discarded.
Process (entering or clearing), stream, vehicle type, and special occurences were contained in the subtitle text. The data was separated into the following elements:
• crossing time of the stop line of all vehicles
• stream and vehicle type of all vehicles
• crossing times of conflict points of the first entering and last clearing vehicle
12 Subtitle Workshop 2.5 by URUsoft, www.urusoft.net
Figure 20:View of the AXIS IP camera from the extension mast
The stop line evaluation was conducted in the same manner. Conflict points could naturally not be observed, due to the camera position. In addition to the crossing times of vehicles, the beginning and ending times of the green vehicle signal was recorded.
5.2.2.3 Controller data
Due to the high camera angle, the signal heads were not visible on the videos from the high angle obser-vations.13 The signal changes had to be recorded separately using the signal controller. The controller data could only be recorded with the precision of one second.
To synchronise video and controller data, a signal head was recorded prior to the extension of the mast with the cameras. The visible signal change together with the current time could then be assigned to a signal change in the controller data. However, the achieved accuracy for times recorded with reference to signal changes (e.g. start-up lost times) didn’t fulfil the requirements. Start-up lost times and crossing times are therefore based on the direct stop line observations.
5.2.2.4 Speed measurements Deployed device and survey setting
The entering and clearance speeds of vehicles was recorded separately for selected approaches using a laser device (Figure 21).14 The device was positioned on the far side of the intersection. The vehicles were mostly measured from the front, thus enabling the detection of the speed of entering vehicles. The
13 In Germany, signal heads are mounted on the respective near side of the intersection, and, thus, are only visible from the according approach.
14 TraffiPatrol XR by ROBOT Visual Systems GmbH, Monheim/Rhein, Germany
5.2 Survey preparation and realisation 77
entering speed was measured in a fast succession (about every one second). In this way, the speed profile and acceleration behaviour of vehicles could be determined.
Figure 21:Speed measurement with TraffiPatrol XR
While through traffic can be measured in this way with high accuracy, the capturing of turning traf-fic is restricted. The Doppler principle used by the device only works precisely with the laser beam pointing in the same direction as the movement. The measured speed vmeas deviates from the true speedvtrue by the reciprocal of the cosine of the angleαbetween movement direction and laser beam direction. The manufacturer recommends a lateral distance of no more than a hundredth of the longi-tudinal distance (equaling about 0.6 °). The measurement error for greater angles is shown in Table 6.
α vvmeas
true error (°) (-) (m/s)
1 ≈1 0.002
2 0.999 0.009 5 0.996 0.057 10 0.985 0.228
Table 6:Speed measurement error depending on the angle αbetween movement direction and laser beam (vtrue=15m/s)
Even for angles of nearly ten degrees the systematic accuracy conforms to the requirements formulated on page 72. The distance is measured with a precision of ten centimetres, the time with a precision of one second. While distance and speed measurements fulfil the desired accuracy, the time measurement does not. Times (namely entering and clearance times) had, therefore, to be derived from distance and speed measurements. The accuracy stated by the manufacturer (Table 35, Appendix B.1.2) fulfills the requirements sufficiently.
Data evaluation
The data generated from the device was imported into spreadsheet software. Invalid measurements were discarded. The remaining data was separated into entering speeds and clearance speeds. Vehicle types, flow directions, and special occurences were recorded on a measurement protocol and added to the electronic data afterwards.
Under saturated conditions, vehicles clear the intersection as a platoon. In this case the speed of all clearing vehicles is similar. If a vehicle cleared the intersection separately from other vehicles, it was marked and evaluated separately. However, this event is an exception and, thus, primarily of importance for safety evaluations.
Moving starts (Type 3 as described on page 44) only exceptionally occured. No significant conclusions could be drawn on the crossing times of these vehicles and their speed.
5.2.2.5 Interaction times
An interaction time occurs, if the entering vehicle adjusts its entering behaviour due to clearing ve-hicles in front of it. If a vehicle starts late or accelerates more slowly than it would without any vehicles in the intersection from the last stage, a time difference ∆tPE reduces the effective green time.
This time difference cannot be observed directly in situ, because the uninfluenced driver’s behaviour is unknown. Whether the observable entering process is influenced by clearing vehicles or not can only indirectly be judged. This analysis requires accurate and comprehensive data on the traffic flow in the intersection. However, the results of the surveys conducted as part of this research did nei-ther deliver the necessary accuracy, nor could a sufficient sample size for such kind of observations be realised.
A methodology to acquire empirical data on the interaction times is presented here as a proposition for further research. Furthermore, based on the surveys the likeliness of interaction times has been estimated, as will be explained in Section 5.3.6.
To estimate interaction times from empirical data, entering times have to be compared for potentially critical situations with situations unlikely involving an interaction. The post-encroachment time can be used to separate the two situations from each other. If a correlation of the entering behaviour with the post-encroachment time can be verified, this correlation gives an indication on the magnitude of interaction times.
The methodology requires, hence, three steps:
1. measurement (or derivation) of post-encroachment time 2. measurement of entering time
3. test for correlation of the two
The post-encroachment time can either be directly measured or calculated from clearance and entering times. The difficulty is that entering time and post-encroachment time have to be measured simultane-ously with high accuracy.
To acquire entering times from the video observations, exact time and distance measurements have to be realised. The time measurements have to be related to the signal timing (namely the beginning of green). Exact measurements of the post-encroachment time imply a reference to a conflict point. This conflict point doesn’t have to be the conflict point for the calculation of the intergreen times. Drivers
5.2 Survey preparation and realisation 79
may perceive a different point as determining for their behaviour. It has, however, to be a more or less unique point for all observations of one kind of conflict to ensure compatibility.
Another issue is the sample size. Because the probability of interaction times is low (cf. Section 5.3.6), the sample size has to be big as to include sufficient situations where interaction times occur. For a conflict, which occurs with a probability of 50 % and a probability that a interaction time occurs for this conflict of 20 % (both being assumptions tending to an overestimation of the probability), in only ten percent of cycles a manoevre time for the respective conflict can be observed. To acquire 20 observations of interaction times, on average 200 cycles have to be evaluated (5 peak hours for a cycle time of 90 s). Due to variations of the occurence of conflicts and interaction times this value can easily reach significantly higher values.
It is apparent that the observations should focus on conflicts with high probability and low absolute intergreen time differences. These conflicts are more relevant and reduce the necessary sample size. The conflict tree of the survey intersection delivers these values.
5.2.3 Realisation