Identify high-risk locations using large-scale real-world connected vehicle data

Transcription

1 Identify high-risk lcatins using large-scale real-wrld cnnected vehicle data Dr. Kun Xie Lecturer in Transprtatin Engineering University f Canterbury Cntact: kun.xie@canterbury.ac.nz Surce: Laird Technlgies

2 Abut University f Canterbury Suthern Alps New Regent Street University f Canterbury is lcated in Christchurch, New Zealand Lytteltn Harbur Btanic Garden

3 Abut University f Canterbury Overview Funded by schlars f Oxfrd and Cambridge universities in 1873 Secnd ldest university in New Zealand 16,906 students in 2018 Ntable Alumni Ernest Rutherfrd the father f atm Āpirana Ngata - the fremst Māri plitician t have ever served in Parliament Helen Cnnn - the first wman student in the British Empire t receive an Hnurs degree Aerial View f Campus

4 Abut University f Canterbury Department f Civil and Natural Resurces Engineering Civil Engineering is ranked 7 th in Academic Ranking f Wrld Universities (ARWU) Abut 250 undergraduates every year Cnnected Traffic Systems Lab 5 faculty members, cvering research areas f traffic safety, netwrk mdeling, signal cntrl, cnnected and autnmus vehicles Bridge Challenge Cnnected Traffic Systems

5 Glbal Safety Issue Rad traffic crashes result in apprximately 1.24 millin deaths and 20 t 50 millin injuries glbally each year (Wrld Health Organizatin, 2013) Rad traffic injuries are the leading cause f death amng yung peple, aged years Number f Rad Traffic Deaths Per Year (Surce: Wrld Health Organizatin 2013)

6 Plans/Calitins fr Rad Safety Surce:

7 Rad Safety in New Zealand In New Zealand, the ttal scial cst f mtr vehicle injury crashes in 2015 is estimated t be $3.8 billin.

8 Rad Safety in New Zealand

9 Rad Safety in New Zealand Safer Jurneys is the gvernment's strategy t guide imprvements in rad safety ver the perid 2010 t Safe System apprach aims fr a mre frgiving rad system that takes human fallibility and vulnerability int accunt. A Safe System apprach

10 Safety Situatin in New Zealand

11 Rad Safety in New Zealand NZ Open Crash Dataset (published in Sep. 2018) Surce:

12 Reactive Safety Slutins In the last few decades, rad safety management heavily relied n histrical crash data. Htspt Identificatin Crash Data Cuntermeasure Develpment Befre-after Evaluatin Essential Tasks f Rad Safety Management

13 Reactive Safety Slutins: An Example Identify Htspts f Pedestrian Crashes Crash Transprtatin Land Use Sci-demgraphic Reference: Xie, K., Ozbay, K., Kurkcu, A., Yang, H., Analysis f traffic crashes invlving pedestrians using big data: investigatin f cntributing factrs and identificatin f htspts. Risk Analysis. Htspts Identified by Ptential fr Safety Imprvement

14 Measure Safety Perfrmance withut Crash Data?

15 Practive Safety Slutins Surrgate Safety Measures (SSMs) are used t quantify risks SSM describes the near-crash scenaris in which a vehicle wuld cllide with anther vehicle if they did nt change their current intentins. Cmmnly used SSMs: Time t cllisin (TTC), Deceleratin rate t avid crash (DRAC) SSM Data Htspt Identificatin Cuntermeasure Develpment Befre-after Evaluatin Detect high-risk lcatins befre the ccurrence f crashes Enable active actins t prevent crashes Evaluate the safety treatments nce they are implemented

16 Practive Safety Slutins: Vide-based safety assessment Cnflicts detected Reference: Xie, K., Li, C., Ozbay, K., Dbler, G., Yang, H., Chiang, A., Wang, Y., Ghandehari, M., Develpment f a cmprehensive framewrk fr vide-based safety assessment. In: Prceedings f the IEEE Intelligent Transprtatin Systems.

17 Cnnected Vehicle Technlgy Cnnected vehicles (CVs) can be seen as mving sensrs in the rad netwrk. Rich infrmatin generated by CVs then can be used t detect and detect high-risk lcatins. CV devices in Michigan Safety Pilt Mdel Deplyment (SPMD) CVs in the rad netwrk ( )

18 Cnnected Vehicle Data Cnnected vehicle data frm Michigan Safety Pilt Mdel Deplyment (SPMD) Scpe Data Acquisitin System (DAS) DataFrntTargets File (4.34 G) Distance Speed difference DataWSU File (11.2 G) Speed f the fllwing vehicle GPS lcatins DataLane File (3.78 G) Basic Safety Message (BSM) (68.2 G unzip) Radside Equipment (29.6 G unzip) Time: April, 2013 Sample rate: 10 Hz A ttal f 62,589,725 messages Lcatins: 75 selected highways DataFrntTargets by Mbileye Heat map f cleaned CV dataset

19 Cnnected Vehicle Data Prcedure f data prcessing

20 Surrgate Safety Measures (SSM) Risky Scenari 1: Speed f the fllwing vehicle is higher than that f the leading vehicle l0 l v TTC = v v 2 1, v ( v v ) 2 v 2 1 therwise 2 1, DRAC = 2( l0 lv ) v v 2 1 therwise Risky Scenari 2: Speed f the fllwing vehicle is lwer than r equal t that f the leading vehicle but the spacing between them is small. A new surrgate safety measure (SSM) is needed

21 Time t Cllisin with Disturbance (TTCD) Time = t 0 Fllwing Vehicle Leading Vehicle Disturbance Assume the disturbance will result in a cnstant deceleratin f the leading vehicle. Time = t 0 + TTCD Cllisin! TTCD: the time interval between the given f the disturbance and the cllisin f tw vehicles Tw pssible cllisin utcmes depending n the deceleratin rate: Outcme 1 the leading vehicle is still decelerating when cllisin ccurs; Outcme 2 the leading vehicle is fully stpped when cllisin ccurs.

22 Time t Cllisin with Disturbance (TTCD) Critical Scenari - the fllwing vehicle cllides with the leading vehicle exactly at the time when the leading vehicle stps l0 + l1 = l2 + lv * l l = v 2d vv = v t = d * * d 2v v v 2 * = 2 v ( l0 l )

23 Time t Cllisin with Disturbance (TTCD) 2v v v Cllisin Outcme 1: d d = l l l0 + l1 = l2 + lv 2 * ( 0 v ) 1 l1 = v1 TTCD d TTCD 2 l2 = v2 TTCD TTCD = 2 ( v v ) + ( v v ) + d ( l l ) v d 2

24 Time t Cllisin with Disturbance (TTCD) 2v v v Cllisin Outcme 2: d d = l l l0 + l1 = l2 + lv l 1 2 v1 = 2d l2 = v2 TTCD 2 * ( 0 v ) 2 ( ) + 2d l l v TTCD = 2dv 0 v 1 2

25 Time t Cllisin with Disturbance (TTCD) T sum up, TTCD can be expressed as: 2 ( v v ) + ( v v ) + 2d ( l l ) 2v v v, d d 2 l0 lv TTCD = 2d ( l l ) + v 2v v v, d 2dv2 2 l0 lv v ( ) v ( ) If TTCD is less than a predefined threshld TTCD*, a cnflict is detected. Define Cnflict Risk with Disturbance (CRD) as the prbability f being invlved with cnflicts under hypthetical disturbance d: CRD = TTCD TTCD * Pr( ) We assume that d fllws a shifted gamma distributin (17.315, 0.128, 0.657) (calibrated by Kuang and Qu (2015) using NGSIM data). Mnte Carl methd is used t cmpute CRD.

26 Crrelatin between Risk Identified by SSMs and Rear-end Crash Data Risk f a car-fllwing scenari identified by SSMs TTC cnflict ccurrence DRAC cnflict ccurrence TTCD CRD (the prbability f cnflict ccurrence) Aggregate trip-based risk int lcatin-based risk Ptential cnfunding effect Risk by SSM? Crash cunts Traffic expsure

27 Crrelatin between Risk Identified by SSMs and Rear-end Crash Data Cntrl fr the traffic expsure effect Risk rate = Crash rate = Risk identifed by each SSM Number f CV GPS pints Rear end crash cunt Traffic vlume Investigate the crrelatin between risk rate and crash rate Risk rate Crash rate X X Traffic expsure

28 Identify the Optimal Threshld fr Each SSM Risks identified by TTC, DRAC and TTCD are subject t the selectin f threshlds T btain the ptimal SSM threshlds, every pssible threshld value incremented by 0.1 within a reasnable range was tested. Crrelatin Significance Test Summary Optimal Threshld Pearsn s crrelatin cefficient P-value TTC 2.3 s DRAC 3.0 m/s TTCD 1.7 s

29 Risk Identified fr One Trip Identified Risks frm 3762 th t 3768 th Time Intervals Time Interval Relative Distance (m) * Relative Speed (m/s) * * Fllwing Vehicle Speed (m/s) Cnflict Presence Identified Cnflict Presence Identified CRD Identified by TTCD by TTC by DRAC

30 Detect High-risk Lcatins Using CV Data TTCD have greater ptential t infer crash risks than AADT data.

31 Detect High-risk Lcatins Using CV Data It shws the ptential f using CV data t detect risk and thus supprts mre practive safety management. High-risk lcatin High-risk lcatin Crashes in April, 2013 Risks identified by TTCD each day in April, 2013

32 Related Study: Smartphne Data fr Risk Detectin Fur dangerus driving behavirs Fast acceleratin, hard breaking, phne use while driving, and speeding Risky Behavirs Histrical Crashes Reference: Yang, D., Xie, K., Ozbay, K., Yang, H., Budnick, N., Zne-based mdeling f time-dependent safety perfrmance using annymized and aggregated smartphne-based dangerus driving event data. Transprtatin Research Bard Annual Cnference, Washingtn, D.C.

33 Related Study: Prevent Secndary Crashes Using Cnnected Vehicles An example f primary and secndary crashes. Use f cnnected vehicles fr reducing secndary crash risk Reference: Yang, H., Wang, Z., Xie, K., Impact f cnnected vehicles n mitigating secndary crash risk. Internatinal Jurnal f Transprtatin Science and Technlgy.

34 Related Study: Prevent Secndary Crashes Using Cnnected Vehicles Simulatin Setting Transmissin range: 1, 000 meters Transmissin frequency: 10 time per secnd N cmmunicatin latency and infrmatin lss Spatitempral Distributin f Cnflicts under Different Market Penetratin Rates (MPRs)

35 Summary and Cnclusins Real-wrld cnnected vehicle pilt test data cllected in Ann Arbr, Michigan was used t generate surrgate safety measures (SSMs) fr risk identificatin. By impsing a hypthetical disturbance, TTCD is able t detect rear-end cllisin risks in varius car fllwing scenaris, even when the leading vehicle has a higher speed. Results shwed that risk data captured by TTCD culd achieve the highest level f crrelatin with histrical rear-end crash data cmpared with ther traditinal SSMs. Cnnected vehicle data has the ptential t advance practive rad safety management.

36 References Xie, K., Yang, D., Ozbay, K., Yang, H., Use f real-wrld cnnected vehicle data in identifying high-risk lcatins based n a new surrgate safety measure. Accident Analysis & Preventin, Xie, K., Ozbay, K., Kurkcu, A., Yang, H., Analysis f traffic crashes invlving pedestrians using big data: investigatin f cntributing factrs and identificatin f htspts. Risk Analysis 37 (8), , Xie, K., Li, C., Ozbay, K., Dbler, G., Yang, H., Chiang, A., Wang, Y., Ghandehari, M.,2016. Develpment f a cmprehensive framewrk fr vide-based safety assessment. In: Prceedings f the IEEE Intelligent Transprtatin Systems, Ri de Janeir, Brazil, Yang, D., Xie, K., Ozbay, K., Yang, H., Budnick, N., Zne-based mdeling f timedependent safety perfrmance using annymized and aggregated smartphne-based dangerus driving event data. Transprtatin Research Bard Annual Cnference, Washingtn, D.C. Yang, H., Wang, Z., Xie, K., Impact f cnnected vehicles n mitigating secndary crash risk. Internatinal Jurnal f Transprtatin Science and Technlgy,

37 Thank Yu! Dr. Kun Xie Lecturer in Transprtatin Engineering University f Canterbury Cntact: kun.xie@canterbury.ac.nz