WHERE DO CYCLISTS RUN RED LIGHTS? AN INVESTIGATION INTO INFRASTRUCTURAL CIRCUMSTANCES Tadej Brezina1, Bernd Hildebrandt2 1 Vienna University of Technology, Institute of Transportation, Research Center of Transport Planning and Traffic Engineering, Vienna, Austria 2 Austrian Transport Safety Board KfV, Department of Traffic Engineering, Vienna, Austria Abstract: Urban cyclists frequently run red traffic lights. This picture is commonly painted by media and public opinion. Our study aims at checking infrastructural influences on such alleged behaviour in order to brighten the view in this controversial public debate. We start by analysing accidents comprising cyclists running red lights. Based on Viennese intersections with an accumulation of red light infringements, the stop-willingness at red lights is studied in-situ. For traffic technological measurements, a total of 23 promising intersections is clustered by physical parameters in 5 settings. The parameters include classified cyclists counts, crossing length, number of crossed lanes, green and red time and compliance with the maximum waiting time according to the Austrian design guidelines. In addition test rides were carried out to estimate the proportion of waiting times to trip times and the traffic light density. The test rides revealed: average distance between traffic lights is 231 m and waiting time is about one quarter (25.9 %) of trip time. 19.2 % of 3,141 observed cyclists run the red light without stopping, while 11.5 % stopped before then running the red light. Therein the proportion of men was higher than with women (28.9 vs. 23.9 %). Cyclists disobeyed red lights about six times more often with demand button traffic lights than with regular traffic lights. As soon as only a single pedestrian crossing lay ahead, infringements happened 4.5 times more frequently than in other circumstances. In comparison to similar traffic volumes cyclis ts are awarded about 30 % less green time than motorized vehicles. Due to repeated de- and acceleration, the high traffic light density and proportion of waiting times have an obstructive impact on attractiveness for cyclists. Based on our findings we recommend measures to improve urban intersection design to facilitate ecologically attentive transport better. Keywords: urban cycling, red traffic lights, cycling infrastructure, cycling safety, behaviour.
1. Introduction A significant number of international studies are dealing with the subject of red light infringements which underlines its high relevance. The share of cyclists running red lights varies considerably. Ortlepp et al. (2008) as well as Johnson et al. (2011) both report that about 7 % of cyclists ran red lights. A range of 27.4 % in Netherlands to 30 % in the USA was reported by van der Meel (2013) and Monsere et al. (2012). The highest share is reported from China, up to 50 % (Wu, C., et al. 2012). Red light riding was found to be the second most frequent infringement among cyclists (Alrutz, D., et al. 2009). However, while 99.6 % of cyclists are aware of the correct behaviour at red lights, 45 % admitted to abide by these rules (Alrutz, D., et al. 2009). Van der Meel (2013) reports 27.4 % of cyclists infringing red lights – depending on location and layout. Alrutz et al. (2009), Johnson et al. (2011) and Van der Meel (2013) both find that males and young aged persons dominate in this behaviour over females and adults and senior citizens. All three studies also investigated infrastructural impacts on red light behaviour. The trends of their findings are summarized in Table 1. Table 1 Infrastructural parameters’ impact on red light riding behaviour from existing literature Infrastructure parameter Impact on red light riding Traffic volume Lower red light cycling share for higher traffic volumes Duration of green phase Lower red light cycling share for longer green phase Duration of red phase Higher red light cycling share for longer red phase Crossing distance Lower red light cycling share for longer crossing distance Riding direction Higher red light cycling share for right turns than for straight ahead rides Sighting distance Lower red light cycling share for limited sighting distances Centre island Higher red light cycling share when centre island is present Velocity of lateral traffic Lower red light cycling share for higher velocity of lateral traffic Composition of lateral traffic Lower red light cycling share for higher share of trucks and busses Between 2000 and 2003 about 18 % of lethally crashed cyclists were involved in red light infringement crash (Lutschounig, S. and Robatsch, K. 2005). Based on Vienna’s total traffic accident database, Fig. 1 and Fig. 2 show distributions of red light infringement casualties of cyclists related to day time, week and month. Along with the general timeline of cycling volume, a definite peak can be found between 3 and 6 pm, while night hours result in very little crashes. During the week, most incidents take place from Monday to Thursday. During the course of the year, the months from May to August show the highest numbers and incidents in fall are more frequent than in spring.
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Fig. 1. Timeline of cyclists’ red light infringement crashes with casualties in Vienna from 2004 to 2013 (n=272)
Fig. 2. Cyclists’ crashes with casualties in Vienna from 2004 to 2013 by days of week (left) and by month (right) (n=272) In principle drivers for red light infringement can be grouped in personal, non-influenceable critera and influenceable traffic engineering criteria. Our task of research was to focus on the latter and find the impact of transport infrastructure design features on the frequency of cyclists infringing red lights. The second section delimits the materials and methods that are used in field study and analysis. Section three shows the results of analysis and the final section provides conclusions from the survey to be used for the improvement of intersection design. 2. Materials and Methods Two survey methods are used: x Seven test rides on a bicycle. These rides criss-crossed densely built-up central Vienna were 3.06 to 6.58 km long and took between 14 and 30 minutes to ride while traversing 14 to 32 traffic lights, x In-situ, stationary measurements of cyclists’ behaviour at 18 pre-selected traffic lights clustered in 5 settings in Vienna. The sites are located in densely built-up central Vienna within or closely outside of the second ring road (Table 1). The choice of intersections for the in-situ measurements is based on intersection typology and layout and on the analysis of disaggregated Viennese crash data (see Table 2). After a brief pre-survey, three sites per setting were chosen to be studied in depth. Table 2 Clustering of 23 survey sites Setting 1 – Cycling path crosses one car lane 2 – Cycling path crosses more than one car lane 3 – Push-button traffic light for cyclists 4 – Cycling path crosses pedestrian path 5 – Cumulation point of cycling crashes after red light violation
Number of pre-selected sites 5 5 5 5 3 110
Number of surveyed sites 3 3 3 3 3
Number of observations 668 943 337 609 584
Sites located in Vienna’s districts 1, 4, 15 1, 4 1, 4, 20 2, 7 8, 9, 16
The in-situ measurements took place for 2 hours per site during weekdays and included the behaviour of 3,141 cyclist in total, mostly males in the age class of 25 to 65 years (Fig. 3. left). In comparison to Vienna’s total distribution (Fig. 3. right), male riders aged 25-65 are over-represented in our sample. Jellinek et al. (2013) reported an age distribution among cyclists as follows: 5 % children younger than 14 years, 13 % of youngsters between 15 and 24 years, 68 % of adults between 25 and 65 and finally 14 % of senior citizens beyond the age of 68.
Fig. 3. Age distribution by sex of observed cyclists (n=3,141) (left) and of Vienna’s total population (n=1,840.226) at January 1st 2016 (right) 3. Results The seven test trips resulted on average in: x 4.3 traffic lights per trip km. x 16 seconds waiting time per intersection. x 71 seconds waiting time per trip km. x 25.9 % as ratio of waiting time to total trip time. x 231 m average distance between traffic lights. Six types of cyclists where distinguished by studying red light behaviour: x Red riders in middle red-phase who do not wait before riding, i.e. crossing without hesitation x Red riders in middle red-phase who wait before riding, i.e. crossing after a full stop x Late riders in early red-phase, crossing shortly after the end of the green phase x Early riders in late red-phase, crossing shortly before the beginning of the green phase x Green riders who wait at red and wait for green before riding. x Green riders who arrive at green and do not wait before riding. For all survey sites the types of observed behaviour is shown in Fig. 4. 1,124 of 3,141 cyclists (35.8 %) ride when their light is still showing red. This red-riding rate varies strongly by location from around 11 % up to 80 %. Compared to the international numbers given in the introduction, Vienna is among the leading group. However, this value is not representative for all of Vienna’s intersections as it was the task to find and survey some of the most problematic sites. More than half of red riders passed without stopping. 35.2 % of riders waited in front of the red light to turn green before continuing to ride. More than half of the red riders crossed without stopping.
Fig. 4. Red and green light riders for all survey sites by type of behaviour (n=3,141) 111
19.2 % of 3.141 ran red lights without stopping, while 11.5 % stopped briefly before running the red light. Therein the proportion of men was higher than with women (28.9 vs. 23.9 %). Cyclists disobeyed red lights about six times more often with demand button traffic lights than with regular traffic lights. If only a pedestrian crossing lay ahead, infringements happened 4.5 times more frequently than in other circumstances. The distinction by sex shows a male prevalence by a factor of 1.2 over female riders. Youngsters ran the red light 1.6 times more frequently than adults. Distinguishing rider types by surveyed settings reveals a more diverse picture (Fig. 5). While all other settings show a disobedience rate of about 30 %, setting three “Push-button traffic light for cyclists” reveals about 70 % of riders running red lights. On the other hand sites from setting five, the culmination points of bicycle crashes, showed the least infringement rates (18.5 %). The share of green riders strongly depends on duration of green and red phases and the coordination of the traffic light with its adjacent traffic lights. At 4 out of 15 surveyed locations, disobedience rates reached more than 50 %.
Fig. 5. Red and green light rider behaviour by setting (n=3,141) By excluding riders that arrive at green from the total sample, the willingness to stop can be derived. In this case, settings three and four show the lowest willingness to stop: 20.0 and 25.1 %. The three remaining settings feature willingness shares bigger than 50 %: from 51.8 to 72.7 %. Settings three and four are sites with Push-button traffic lights for cyclists and intersections where cycling paths cross only pedestrian paths. Where the red phase lasted 60 seconds or longer, cyclists run the red light 2.7 times more often than in sites with shorter periods. At push-button traffic lights (setting 3) the disobedience rate was 6 times higher and at intersections with only a pedestrian walkway it was 4.5 times higher than in the remaining settings. Finally we analysed the sites’ impact on relation between share of cyclists arriving at green and the ratio of green to red phase time (Fig. 6). In addition we plotted the relation of average waiting time to green time coefficient over the share of red riders (Fig. 7). These diagrams show: x That cyclists tend to arrive at green lights more frequently when the green time’s share increases, x That cyclists tend to infringe red lights more often when average waiting time increases over green-time. Average waiting time is better suited for explanatory means than red phase duration as it implies the coordination with preceding traffic lights.
Fig. 6. Green to red time ratio over green riders without waiting time (n=15) 112
Fig. 7. Average waiting time to red time ratio over red riders (n=18) 4. Discussion and Conclusion The high density of traffic lights and the long waiting times considerably reduce the potential for cycling in Vienna. Under the premise of travel time stability, waiting times in extent of 25 % of total trip time reduce possible trip lengths by about one quarter. In addition, recurring bicycle de- and acceleration due to the high traffic light density of 4.3 traffic lights per km increases rider energy expenditure. Applied to car traffic only, ease of traffic flow is considered the highest priority. For the same amount of vehicle throughput, this reduces green time for cyclists by 70 %. The crucial advantage of higher throughput of cyclists per unit of width is thus forfeited. As 14 out of 23 traffic lights showed red phases longer than 40 seconds, a large portion of studied traffic lights can be considered as cyclist-unfriendly. A low acceptance (inverse figure of disobedience) asks to be interpreted as indicator of low bicycle friendliness. Settings three and four with their significantly high disobedience rates teach us that such designs shall be avoided. Only 1 out of 1,124 observed red cyclists had a conflict situation with a car driver. Red cyclists cautiously observed crossing traffic regarding approaching vehicles and their time gaps before infringing the red light. The limitations of the study at hand suggest in-depth work to implement improvements and perform the following measurements: x Time gaps between crossing vehicles, x Velocities of crossing vehicles, x Sizes of arriving cyclist groups, x Distinction between cyclists riding straight on and those turning right into the crossing traffic. Based on our findings we suggest measures to improve urban intersections insofar that infrastructure design supports ecologically attentive transport. Our recommendations are: x Reduce density of traffic lights in general, x Reduce average waiting times to below 40 seconds, x Avoid waiting times of 60 seconds or longer, x Give cyclists traffic lights of their own instead of combining the with a pedestrian lights, x Avoid push-button traffic lights, x Introduce coordinated traffic lights for cyclists – “green wave”, x Study and then introduce right turn on red for cyclists, as it is already common in many other countries (Netherlands, France, Belgium or Switzerland). Acknowledgements Thank you to Eleonora Balaoura, Manuela Winder and Helmut Lemmerer for proof-reading. References Alrutz, D.; Bohle, W.; Müller, H.; Prahlow, H.; Hacke, U.; Lohmann, G. 2009. Unfallrisiko und Regelakzeptanz von Radfahrern; BAST; Bergisch Gladbach, Germany.
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Jellinek, R.; Hildebrandt, B.; Pfaffenbichler, P.C.; Lemmerer, H. 2013. MERKUR - Auswirkungen der Entwicklung des Marktes für E-Fahrräder auf Risiken, Konflikte und Unfälle auf Radinfrastrukturen, Österreichischer Verkehrssicherheitsfonds, Wien, Austria. Johnson, M.; Newstead, S.; Charlton, J.; Oxley, J. 2011. Riding through red lights: The rate, characteristics and risk factors of non-compliant urban commuter cyclists, Accident Analysis & Prevention, 43(1): 323-328. Lutschounig, S.; Robatsch, K. 2005. Rotlichtüberfahrer, Zeitschrift für Verkehrsrecht; 41(4): 141-144. Monsere, C.; Figliozzi, M.; Thompson, S. 2012. Operational guidance for bicycle-specific traffic signals in the United States: A review - Interim Report #1; Oregon Department of Transportation and Federal Highway Administration; Portland, U.S.A. Ortlepp, J.; Neumann, V.; Utzmann, I. 2008. Verbesserung der Verkehrssicherheit in Münster - Schlussbericht; Gesamtverband der Deutschen Versicherungswirtschaft e.V, Berlin, Germany. Van der Meel, E.M. 2013. Red light running by Cyclists: Which factors influence the red light running by cyclists?. Master's Thesis. Civil Engineering and Geosciences, TU Delft; Delft, Netherlands. Wu, C.; Yao, L.; Zhang, K. 2012. The red-light running behavior of electric bike riders and cyclists at urban intersections in China: An observational study, Accident Analysis & Prevention, 49: 186-192.
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