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Road Traffic Incident Risk Assessment. Accident Data Pilot on Ring I of the Helsinki Metropolitan Area

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Road Traffic Incident Risk Assessment. Accident Data Pilot on Ring I of the Helsinki Metropolitan Area NOL CH OG E Y T • • R E E C S N E E A I 172 R C C S H • S H N I G O I H S L I I V G • H S T Road traffic incident risk assessment Accident data pilot on Ring I of the Helsinki Metropolitan Area Satu Innamaa | Ilkka Norros | Pirkko Kuusela | Riikka Rajamäki | Eetu Pilli-Sihvola VTT TECHNOLOGY 172 Road traffic incident risk assessment Accident data pilot on Ring I of the Helsinki Metropolitan Area Satu Innamaa, Ilkka Norros, Pirkko Kuusela, Riikka Rajamäki & Eetu Pilli-Sihvola ISBN 978-951-38-8257-0 (URL: http://www.vtt.fi/publications/index.jsp) VTT Technology 172 ISSN-L 2242-1211 ISSN 2242-122X (Online) Copyright © VTT 2014 JULKAISIJA – UTGIVARE – PUBLISHER VTT PL 1000 (Tekniikantie 4 A, Espoo) 02044 VTT Puh. 020 722 111, faksi 020 722 7001 VTT PB 1000 (Teknikvägen 4 A, Esbo) FI-02044 VTT Tfn +358 20 722 111, telefax +358 20 722 7001 VTT Technical Research Centre of Finland P.O. Box 1000 (Tekniikantie 4 A, Espoo) FI-02044 VTT, Finland Tel. +358 20 722 111, fax +358 20 722 7001 Road traffic incident risk assessment Accident data pilot on Ring I of the Helsinki Metropolitan Area Tieliikenteen häiriöriskin arviointi. Pilotti Kehä I:n onnettomuusaineistolla. Satu Innamaa, Ilkka Norros, Pirkko Kuusela, Riikka Rajamäki & Eetu Pilli-Sihvola. Espoo 2014. VTT Technology 172. 49 p. + app. 8 p. Abstract The purpose of this project was to apply the Palm distribution to the analysis of riskiness of different traffic and road weather conditions introduced in a previous project (Innamaa et al. 2013), develop the method further, and find factors that statistically significantly affect traffic incident risk. The method was piloted using data from Ring-road I of the Helsinki Metropoli- tan Area. The study was based on registered accidents that occurred on Ring- road I in 2008–2012, totalling 1120. In addition to accident data, traffic data from eight automatic traffic measurement stations (inductive loops) and road weather data were also used. The basic methodological idea was to compare the traffic and weather circum- stances just before an accident with the Palm probability of the same circum- stances. The notion of Palm probability comes from the theory of random point processes, and means the probability distribution "seen" by a randomly selected point of the point process, i.e. the driver in this case (in contrast to the stationary probability, which is the probability distribution seen at a random time point). If each car driver had a constant stochastic intensity of causing an accident, then the accident circumstances would follow the Palm distribution. The idea of the method applied here is to assess the influence of circumstances on incidents by compar- ing the incident circumstance distribution with the Palm distribution of circum- stances: differences between these distributions hint at effects of circumstances on accidents. The results showed that there were several specific weather conditions that were more common among drivers who were involved in an accident than among drivers in general. These conditions included air temperature from –6 degrees Celcius down, snowfall or heavy rain, limited visibility, and snowy or wet road surface. The results further showed that the probability of an accident is higher in conditions when a weather alarm is issued by the Transport Agency (the road operator) than in general. In addition, in weekday afternoon traffic (15–17 o’clock) the risk of accident was found to be 50% higher than generally. In night time traffic (2–5 o’clock) the risk was even higher. The results indicated that the traffic situation correlated poorly with accident risk. However, the results related to the traffic situation can be con- sidered only indicative due to inaccuracies in the accident location information and sparseness of the traffic detector network. 3 In conclusion, the findings suggest that the proposed method for identifying conditions where accident risk is elevated by comparing the traffic and weather circumstances just before the accident with the “Palm probability” of the same circumstances indeed works. Not all results were statistically significant due to some circumstances being rare. However, with the calculation of risk levels and Kullback-Leibler divergence, it was possible to assess the findings. Keywords traffic incident risk, Palm probability, driving conditions 4 Tieliikenteen häiriöriskin arviointi Pilotti Kehä I:n onnettomuusaineistolla Road traffic incident risk assessment. Accident data pilot on Ring I of the Helsinki Metropolitan Area. Satu Innamaa, Ilkka Norros, Pirkko Kuusela, Riikka Rajamäki & Eetu Pilli-Sihvola. Espoo 2014. VTT Technology 172. 49 s. + liitt. 8 s. Tiivistelmä Tämän hankkeen tavoitteena oli soveltaa Palm-jakaumaan perustuvaa aikaisem- massa hankkeessa (Innamaa ym. 2013) kehitettyä menetelmää analysoida erilai- siin liikenne- ja keliolosuhteisiin liittyvää liikenteen häiriöiden riskiä, kehittää mene- telmää eteenpäin ja löytää tekijöitä, jotka vaikuttavat liikenteen häiriöiden riskiin tilastollisesti merkitsevästi. Menetelmää kokeiltiin Kehä I:llä kootulla aineistolla. Tutkimus perustui Kehä I:llä vuosina 2008–2012 tapahtuneisiin, rekisteröityihin onnettomuuksiin, joita oli yhteensä 1120. Tutkimuksessa käytettiin onnettomuusaineiston lisäksi liikennetie- toja kahdeksasta liikenteen automaattisesta mittauspisteestä (induktioilmaisimia) ja tiesääaseman tuottamaa tietoa. Menetelmällinen perusajatus oli verrata liikenne- ja sää-/keliolosuhteita hetkeä ennen onnettomuutta samojen olosuhteiden Palm-todennäköisyyksiin. Palm- todennäköisyyden käsite tulee satunnaisen pisteen prosessoinnin teoriasta ja tarkoittaa satunnaisesti valitun prosessointipisteen, tässä tapauksessa autoili- jan, ”näkemää” todennäköisyysjakaumaa (vastakohtana stationaariselle todennä- köisyydelle, joka kuvaa todennäköisyysjakaumaa satunnaisella ajanhetkellä). Jos jokaisella autoilijalla on vakiosuuruinen stokastinen intensiteetti aiheuttaa onnet- tomuus, onnettomuusolosuhteet noudattavat Palm-jakaumaa. Menetelmässä on sovellettu ideaa arvioida olosuhteiden vaikutus liikenteen häiriöihin vertaamalla häiriöiden olosuhdejakaumaa olosuhteiden Palm-jakaumaan: Näiden jakaumien väliset erot viittaavat olosuhteiden onnettomuusvaikutukseen. Tulokset osoittavat useita sää- ja keliolosuhteita, jotka olivat yleisempiä onnet- tomuuksiin joutuneiden ajoneuvojen kohdalla kuin yleensä. Tällaisia olosuhteita olivat korkeintaan –6º C ilman lämpötilan lumisade tai rankka vesisade, huono näkyvyys ja luminen tai märkä tienpinta. Lisäksi tulokset osoittivat, että onnetto- muuden todennäköisyys on korkeampi olosuhteissa, jolloin Liikennevirasto antaa kelivaroituksen, kuin muuten. Lisäksi arki-iltapäivien (klo 15–17) liikenteessä onnettomuusriski oli 50 % kor- keampi kuin yleensä. Yöaikaan (klo 2–5) riski oli vielä korkeampi. Tulokset osoitti- vat, että liikennetilanne korreloi huonosti onnettomuusriskin kanssa. Liikennetilan- teeseen liittyviä tuloksia voi kuitenkin pitää vain suuntaa-antavina onnettomuuden paikkatiedon epäluotettavuuden ja harvan liikenneilmaisimien verkon takia. Yhteenvetona voidaan todeta tulosten viittaavan siihen, että ehdotettu menetelmä toimii eli että menetelmä tunnistaa olosuhteet, jolloin onnettomuusriski on kohon- nut vertaamalla liikenne- ja sää-/keliolosuhteita hetki ennen onnettomuutta samo- 5 jen onnettomuuksien Palm-jakaumaan. Kaikki tulokset eivät olleet tilastollisesti merkitseviä olosuhteiden harvinaisuuden takia. Tulokset voitiin kuitenkin arvioida laskemalla riskitasoja ja Kullback-Leibnerin divergenssi. Avainsanat traffic incident risk, Palm probability, driving condition 6 Preface A research project funded by the Finnish Transport Agency launched the develop- ment of a method for identifying conditions in which the risk of a traffic incident is elevated. VTT Technical Research Centre of Finland aimed to develop the method further and validate the proposed method by testing it with a wider dataset. Satu Innamaa led the project, and with Riikka Rajamäki provided the transport engineering perspective. She structured the deliverable and wrote the Introduction and Discussion. Riikka Rajamäki provided raw data for the study and wrote the Data chapter. Mikko Kallio processed the raw data for analysis. Ilkka Norros and Pirkko Kuusela were in charge of developing and applying the mathematical method (Chapters Mathematical Method, Results and Appendix A). Eetu Pilli- Sihvola was responsible for searching the literature for the review. 7 Contents Abstract ........................................................................................................... 3 Tiivistelmä ....................................................................................................... 5 Preface ............................................................................................................. 7 List of symbols .............................................................................................. 10 1. Introduction ............................................................................................. 11 1.1 Introduction ...................................................................................... 11 1.2 Literature review ............................................................................... 11 1.2.1 Effect of traffic conditions on incident risk................................ 11 1.2.2 Effect of weather conditions and other factors ......................... 13 1.2.3 Secondary incident risk estimation.......................................... 14 1.2.4 Summary of the
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