In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators

Figures & data

Figure 1. Severity levels of traffic events. Adapted from Hydén (1987) and Svensson (1998).

Table 1. Factors affecting collision risk and injury risk, respectively.

Collison risk	Injury risk
Closeness in time Closeness in space Speeds of the involved road users	Speed differences Mass differences Relative angle Fragility of the involved road users

Source: Based on Svensson (1998).

Figure 2. Causal model. Adopted from Davis et al. (2011).

Table 2. Validation studies of TTC-based indicators.

Reference	Indicator	Validation method	Number of locations	Duration of observation per location (h)	VRU included
Hydén (1977)	TA < 1.5s	Variance of conflict/accident ratio	115	8	Yes
Lord (1996)	TA < 1.5s	Correlation between accident model and observed number of conflicts	8	8	Yes
Sacchi et al. (2013)	TTC < 3s	Comparison between accident based and conflict based before and after study	3	16	No
El-Basyouny and Sayed (2013)	TTC < 1.5s	Correlation between SPF based on conflicts and recorded accidents	51	16	Yes
Sacchi and Sayed (2016)	TTC < 1.5s	Correlation between SPF based on conflicts and recorded accidents	49	16	Yes

Note: TA = time to accident, TTC = time to collision, SPF = safety performance function.

Table 3. Validation studies of PET-based indicators.

Reference	Indicator	Validation method	Number of locations	Duration of observation per location (h)	VRU included
Cooper (1984) (1)	PET < 3s	Linear correlation between critical events and recorded accidents	5	10.5	No
Cooper (1984) (2)	PET < 3s	Linear correlation between critical events and recorded accidents	7	10.5	No
Lord (1996)	PET < 3s	Linear correlation between SPF and number of critical events	8	8	Yes
Songchitruksa and Tarko (2006)	PET (all events)	Extreme value theory	18	8	No
Peesapati, Hunter, and Rodgers (2013)	PET (< 1 – 3s)	Linear correlation between critical events and recorded accidents	18	5	No
Zheng, Ismail, and Meng (2014b)	PET (all events)	Extreme value theory	21	3	No
Zheng et al. (2014a)	PET (all events)	Extreme value theory	29	3	No
Alhajyaseen (2015)	CI	Linear correlation between critical events and recorded accidents	5	3	No

Note: PET = post-encroachment time, CI = conflict index, SPF = safety performance function.

Table 4. Validation studies of the American TCT

Reference	Validation method	Number of locations	Duration of observation per location (h)	VRU included
Baker (1972)	Linear correlation between critical events and recorded accidents	392	8	No
Paddock and Spence (1973)	Linear correlation between reported accidents and an SPF based on flow and conflicts	611	8	No
Pugh and Halpin (1974)	Linear correlation between critical events and recorded accidents	240	5	No
Migletz et al. (1985)	Correlation between SPF and number of conflicts	46	48	No
Lord (1996)	Correlation between accidents model and observed number of conflicts	8	8	Yes

Note: SPF = safety performance function.

Table 5. Validation studies of the Canadian TCT.

Reference	Validation method	Number of locations	Duration of observation per location (h)	VRU included
Brown et al. (1984)	Linear correlation between critical events and recorded accidents	4	16	Yes
Brown (1994)	Linear correlation between critical events and recorded accidents and variance of conflict/accident ratio	13	16	Yes
Sayed and Zein (1999)	Linear correlation between critical events and recorded accidents	94	16	Yes

Table 6. Validation studies of the Dutch TCT

Reference	Validation method	Number of locations	Duration of observation per location (h)	VRU included
van der Horst et al. (2007)	Process validation: comparison of video observed conflicts and accidents	4	24	No
van der Horst et al. (2016)	Comparison between accident based and conflict based before and after study	4	4.5	Yes

Table 7. Validation studies of the Swedish TCT.

Reference	Validation method	Number of locations	Duration of observation per location	VRU included
Hydén (1987)	Process validation: comparison of conflicts and in-depth accident data	Unspecified locations	Unspecified locations	Yes
Svensson (1992)	Variance of conflict/accident ratio	115	8 h	Yes
Shbeeb (2000)	Variance of conflict/accident ratio	42	12 h	Yes

Table 8. Validation studies of TCTs using only a subjective severity rating.

Reference	Indicator	Validation method	Number of locations	Duration of observation per location (h)	VRU included
Spicer (1973)	The British TCT	Linear correlation	6	10	No
TRRL (1980)	The British TCT	Linear correlation	14	10	No
Cooper (1973)	The British TCT	Linear correlation	59	28	No

Table 9. Summary of surrogate safety indicators and their relation to collision risk and injury risk.

	Collision risk			Injury risk	No. of validation studies
	Initial conditions	Evasive actions	Outcomes		All	Including VRUs
TTC-based indicators (Hayward, 1971; Minderhoud & Bovy, 2001; van der Horst, 1990)	No	No	Yes	No	3	2
Time to accident (Hydén, 1977)	Yes	No	No	No	2	2
PET-based indicators (Allen & Cooper, 1978; Hansson, 1975; Laureshyn et al., 2010; Mohamed & Saunier, 2013)	No	No	Yes	No	7	1
Conflict index (Alhajyaseen, 2015)	No	No	Yes	Yes	1	0
Deceleration-based indicators (Balasha, Hakkert, & Livneh, 1979; Gettman & Head, 2003; Hupfer, 1997)	No	Yes	No	No	0	0
Yaw rate (Tageldin et al., 2015)	No	Yes, for motorcyclists	No	No	0	0
Pedestrian risk index (Cafiso et al., 2011)	Yes	Yes, for motor vehicles	-	Yes	0	0
Conflict severity (Bagdadi, 2013)	Yes	No	-	Yes	0	0
DeltaV (Shelby, 2011)	No	No	No	Yes	0	0
Extended DeltaV (Laureshyn et al., 2017)	No	No	Yes	Yes	0	0
Change in step frequency (Tageldin & Sayed, 2016)	No	Yes, for pedestrians	No	No	0	0
Aggregated crash index (Kuang et al., 2015)	Yes	Yes	–	No	0	0
Space occupancy index (Ogawa, 2007)	Yes	No	No	No	0	0
The American TCT (Parker & Zegeer, 1989; Perkins & Harris, 1967)	No	Yes	No	No	4	0
The Canadian TCT (Brown et al., 1984; Sayed & Zein, 1999)	No	Yes*	Yes	No	3	3
The Dutch TCT (Kraay, 1987; Kraay et al., 2013)	Yes*	Yes*	Yes	Yes*	2	2
The Swedish TCT (Hydén, 1987; Svensson, 1992)	Yes	No	–	No	3	3
The British TCT (Baguley, 1984; Kaparias et al., 2010)	Yes*	Yes*	–	Yes*	3	0

*The asterisk indicates that the indicator relies on a subjective component.

Table

Abbreviation	Indicator name	Description	Reference
TTC	Time to collision	The time remaining before the collision if the road users continue with their respective speeds and trajectories.	Hayward (1971) and van der Horst (1990)
TTCmin	Minimum time to collision	The minimum TTC value calculated in an event	Hayward (1971) and van der Horst (1990)
TA	Time to accident	The TTC value the moment an involved road user takes an evasive action	Hydén (1977)
TTZ	Time to zebra	The time remaining before a road user reaches the zebra crossing	Várhelyi (1998)
TTL	Time to lane crossing	The time remaining before a road user	Chin et al. (1992)
	The reciprocal of TTC	One divided by the TTC value	Chin et al. (1992)
TET	time-exposed time to collision	The time during an encounter when the TTC is below a certain threshold value	Minderhoud and Bovy (2001)
TIT	time-integrated time to collision	The area between the threshold level and the TTC curve when it drops below a certain threshold value	Minderhoud and Bovy (2001)
T2	T2	The predicted arrival time of the second road user, calculated while the first road user has not left the conflict point yet.	Laureshyn et al. (2010)
PET	Post-encroachment time	The time between the moment when the first road user leaves the path of the second and the moment when the second reaches the path of the first	Allen and Cooper (1978)
GT	Gap time	The time between the entries into the conflict spot of two vehicles	Allen and Cooper (1978)
ET	Encroachment time	The time when the first vehicle entering the conflict area infringes on the predicted path of the second vehicle	Allen and Cooper (1978)
TAdv	Time advantage	The predicted PET value, provided that the road users continue with their paths and speeds	Hansson (1975)
CI	Conflict index	A combination of PET with the speed, mass and angle of the involved road users to estimate the released kinetic energy in a collision.	Alhajyaseen (2015)
DR	Deceleration rate	The magnitude of the deceleration action of a driver the moment he or she begins an evasive braking manoeuvre	Gettman and Head (2003)
DST	Deceleration to safety time	The minimal necessary deceleration for a driver to avoid the collision	Hupfer (1997)
	Maximum absolute rate of change in the step frequency	Maximum absolute rate of change in the step frequency	Tageldin and Sayed (2016)
	Jerk profile	The time derivative of acceleration	Tageldin et al. (2015)
	Yaw rate	The angular velocity of the road users’ rotation	Tageldin et al. (2015)
PRI	Pedestrian risk index	A combination of TTZ with the driver’s reaction time and the braking capabilities of the vehicle	Cafiso et al. (2011)
DeltaV	DeltaV	The change in velocity forced on the road users because of a collision	Shelby (2011)
CS	Conflict severity	A combination of the indicators DeltaV, TA and the assumed maximum average deceleration	Bagdadi (2013)
Extended DeltaV	Extended DeltaV	A combination of DeltaV with the T2 indicator and a deceleration constant	Laureshyn et al. (2017)
ACI	Aggregated crash index	A combination of several indicators meant to represent the overall crash risk.	Kuang et al. (2015)
	Space occupancy index	Incursions into a safety area which is calculated around each road user	Ogawa (2007)

Hydén, C. (1987). The development of a method for traffic safety evaluation: The Swedish traffic conflict technique (Doctoral thesis). Lund University.

 Google Scholar

Svensson, Å. (1998). A method for analysing the traffic process in a safety perspective (Doctoral thesis), University of Lund, Lund Institute of Technology.

 Google Scholar

Davis, G. A., Hourdos, J., Xiong, H., & Chatterjee, I. (2011). Outline for a causal model of traffic conflicts and crashes. Accident Analysis & Prevention, 43(6), 1907–1919. doi: 10.1016/j.aap.2011.05.001

 PubMed Web of Science ®Google Scholar

Hydén, C. (1977). A traffic conflict technique for determining risk (Report No. LUTVDG/(TVTT-3005)/1-95/(1977) & Bulletin 15 B).

 Google Scholar

Lord, D. (1996). Analysis of pedestrian conflicts with left-turning traffic. Transportation Research Record, 1538, 61–67. doi:10.3141/1538-08 doi: 10.1177/0361198196153800108

 Google Scholar

Sacchi, E., Sayed, T., & de Leur, P. (2013). A comparison of collision-based and conflict-based safety evaluations: The case of right-turn smart channels. Accident Analysis & Prevention, 59, 260–266. doi: 10.1016/j.aap.2013.06.002

 PubMed Web of Science ®Google Scholar

El-Basyouny, K., & Sayed, T. (2013). Safety performance functions using traffic conflicts. Safety Science, 51(1), 160–164. doi: 10.1016/j.ssci.2012.04.015

 Web of Science ®Google Scholar

Sacchi, E., & Sayed, T. (2016). Conflict-based safety performance functions for predicting traffic collisions by type. Transportation Research Record: Journal of the Transportation Research Board, 2583, 50–55. doi: 10.3141/2583-07

 Web of Science ®Google Scholar

Cooper, P. J. (1984). Experience with traffic conflicts in Canada with emphasis on “post encroachment time” techniques. In E. Asmussen (ed.) International Calibration Study of Traffic Conflict Techniques. NATO ASI Series (Series F: Computer and Systems Sciences) (Vol. 5). Berlin, Heidelberg: Springer.

 Google Scholar

Songchitruksa, P., & Tarko, A. (2006). The extreme value theory approach to safety estimation. Accident Analysis and Prevention, 38, 811–822. doi: 10.1016/j.aap.2006.02.003

 PubMed Web of Science ®Google Scholar

Peesapati, L. N., Hunter, M. P., & Rodgers, M. O. (2013). Evaluation of postencroachment time as surrogate for opposing left-turn crashes. Transportation Research Record, 2386, 42–51. doi: 10.3141/2386-06

 Web of Science ®Google Scholar

Zheng, L., Ismail, K., & Meng, X. (2014b). Shifted Gamma-Generalized Pareto Distribution model to map the safety continuum and estimate crashes. Safety Science, 64, 155–162. doi: 10.1016/j.ssci.2013.12.003

 Web of Science ®Google Scholar

Zheng, L., Ismail, K., & Meng, X. (2014a). Freeway safety estimation using extreme value theory approaches: A comparative study. Accident Analysis & Prevention, 62, 32–41. doi: 10.1016/j.aap.2013.09.006

 PubMed Web of Science ®Google Scholar

Alhajyaseen, W. K. M. (2015). The integration of conflict probability and severity for the safety assessment of intersections. Arabian Journal for Science and Engineering, 40(2), 421–430. doi: 10.1007/s13369-014-1553-1

 Web of Science ®Google Scholar

Baker, William T. (1972). An evaluation of the traffic conflicts technique. 51st Annual Meeting of the Highway Research Board. Washington District of Columbia, United States.

 Google Scholar

Paddock, R. D., & Spence, D. E. (1973). The conflicts technique: An accident prediction method. Transportation Research Record 486 (pp. 1–10). Ohio Department of Transportation, Columbus, United States.

 Google Scholar

Pugh, D. E., & Halpin, T. J. (1974). Traffic conflicts in Washington State. Washington State Department of Highways, Washington.

 Google Scholar

Migletz, D. J., Glauz, W. D., & Bauer, K. M. (1985). Relationships between traffic conflicts and accidents (Report No. FHWA/RD-84/042).

 Google Scholar

Brown, G., Chau, A., DeCastilho, B., & Navin, F. (1984). Land transport safety series, The University of British Columbia, Department of Civil Engineering.

 Google Scholar

Brown, G. R. (1994). Traffic conflicts for road user safety studies. Canadian Journal of Civil Engineering, 21(5), 1–15. doi: 10.1139/l94-001

 Web of Science ®Google Scholar

Sayed, T., & Zein, S. (1999). Traffic conflict standards for intersections. Transportation Planning & Technology, 22, 309–323. doi: 10.1080/03081069908717634

 Web of Science ®Google Scholar

van der Horst, A. R. A., Rook, A. M., Amerongen, P. J. M. v., & Bakker, P. J. (2007). Video-recorded accidents, conflicts and road user behaviour: Integral Approach Analysis of Traffic Accidents (IAAV). TNO Defence, Security and Safety.

 Google Scholar

van der Horst, A. R. A., Thierry, M. C., Vet, J. M., & Rahman, A. F. (2016). An evaluation of speed management measures in Bangladesh based upon alternative accident recording, speed measurements, and DOCTOR traffic conflict observations. Transportation Research Part F. doi: 10.1016/j.trf.2016.05.006

 Google Scholar

Svensson, Å. (1992). Vidareutveckling och validering av den svenska konflikttekniken (Report No. (unpublished)).

 Google Scholar

Shbeeb, L. (2000). Development of a traffic conflicts technique for different environments – a comparative study of pedestrian conflicts in Sweden and Jordan (Doctoral thesis). University of Lund, Lund Institute of Technology.

 Google Scholar

Spicer, B. R. (1973). A study of traffic conflicts at six intersections (Report No. Report LR551). Retrieved from http://trid.trb.org/view/129891.

 Google Scholar

TRRL. (1980). Traffic conflicts and accidents at road junctions. TRRL Leaflet LF918.

 Google Scholar

Cooper, P. J. (1973). Predicting intersection accidents: The use of conflicts and other models based on traffic flow parameters to predict accident experience at non-signalised intersections, Ottawa, Canada.

 Google Scholar

Hayward, J. C. (1971). Near misses as a measure of safety at urban intersections (Master thesis). The Pennsylvania State University.

 Google Scholar

Minderhoud, M. M., & Bovy, P. H. L. (2001). Extended time-to-collision measures for road traffic safety assessment. Accident Analysis & Prevention, 33(1), 89–97. doi: 10.1016/S0001-4575(00)00019-1

 PubMed Web of Science ®Google Scholar

van der Horst, A. R. A. (1990). A time-based analysis of road user behaviour in normal and critical encounters (Doctoral thesis), Delft University of Technology.

 Google Scholar

Allen, Brian, Shin, Tom, & Cooper, Peter. (1978). Analysis of traffic conflicts and collisions. Transportation Research Record, 667, 67–74.

 Google Scholar

Hansson, A. (1975). Studies in driver behaviour, with applications in traffic design and planning. Two examples (Report No. Bulletin 11).

 Google Scholar

Laureshyn, A., Svensson, Å., & Hydén, C. (2010). Evaluation of traffic safety, based on micro-level behavioural data: theoretical framework and first implementation. Accident Analysis & Prevention, 42, 1637–1646. doi: 10.1016/j.aap.2010.03.021

 PubMed Web of Science ®Google Scholar

Mohamed, M. G., & Saunier, N. (2013). Motion prediction methods for surrogate safety analysis. Transportation Research Record, 2386, 168–178. doi: 10.3141/2386-19

 Web of Science ®Google Scholar

Balasha, D., Hakkert, A. S., & Livneh, M. (1979). A quantitative definition of the near accident concept. Second International Traffic Conflicts Technique Workshop, Paris.

 Google Scholar

Gettman, D., & Head, L. (2003). Surrogate safety measures from traffic simulation models (Report No. FHWA-RD-03-050).

 Google Scholar

Hupfer, C. (1997). Deceleration to Safety Time (DST) – a useful figure to evaluate traffic safety? 10th ICTCT Workshop, Lund.

 Google Scholar

Tageldin, A., Sayed, T., & Wang, X. (2015). Can time proximity measures be used as safety indicators in all driving cultures? A case study of motorcycle safety in China. 94th TRB Annual Meeting, Washington, DC.

 Google Scholar

Cafiso, S., García, A. G., Cavarra, R., & Rojas, M. A. R. (2011). Crosswalk safety evaluation using a Pedestrian Risk Index as traffic conflict measure. 3rd International Conference on Road Safety and Simulation, Indianapolis, IN.

 Google Scholar

Bagdadi, O. (2013). Estimation of the severity of safety critical events. Accident Analysis & Prevention, 50, 167–174. doi: 10.1016/j.aap.2012.04.007

 PubMed Web of Science ®Google Scholar

Shelby, S. G. (2011). Delta-V as a measure of traffic conflict severity. 90th TRB Annual Meeting, Washington, DC.

 Google Scholar

Laureshyn, A., De Ceunynck, T., Karlsson, C., Svensson, Å., & Daniels, S. (2017). In search of the severity dimension of traffic events: Extended Delta-V as a traffic conflict indicator. Accident Analysis & Prevention, 98, 46–56. doi: 10.1016/j.aap.2016.09.026

 PubMed Web of Science ®Google Scholar

Tageldin, A., & Sayed, T. (2016). Developing evasive action-based indicators for identifying pedestrian conflicts in less organized traffic environments. Journal of Advanced Transportation, 50, 1193–1208. doi: 10.1002/atr.1397

 Web of Science ®Google Scholar

Kuang, Y., Qu, X., & Wang, S. (2015). A tree-structured crash surrogate measure for freeways. Accident Analysis & Prevention, 77, 137–148. doi: 10.1016/j.aap.2015.02.007

 PubMed Web of Science ®Google Scholar

Ogawa, K. (2007). An analysis of traffic conflict phenomenon of bicycles using space occupancy index. Journal of the Eastern Asia Society for Transportation Studies, 7, 1820–1827.

 Google Scholar

Parker, M. R., & Zegeer, C. V. (1989). Traffic conflict techniques for safety and operation (Report No. FHWA-IP-88-027).

 Google Scholar

Perkins, S. R., & Harris, J. I. (1967). Traffic conflict characteristics: Freeway curve and exit area F1, December, 1966. Retrieved from: http://trid.trb.org/view/618537.

 Google Scholar

Kraay, J. H. (1987). The Dutch conflict observation method “doctor”. Retrieved from http://trid.trb.org/view/291273

 Google Scholar

Kraay, J. H., van der Horst, A. R. A., & Oppe, S. (2013). Manual conflict observation technique doctor. Foundation Road Safety for All.

 Google Scholar

Baguley, C. J. (1984). The British traffic conflicts technique. NATO advanced research workshop on international calibration study of traffic conflict techniques, Copenhagen.

 Google Scholar

Kaparias, I., Bell, M. G. H., Greensted, J., Cheng, S., Miri, A., Taylor, C., & Mount, B. (2010). Development and implementation of a vehicle–pedestrian conflict analysis method: Adaptation of a vehicle–vehicle technique. Transportation Research Record, 75–82. doi: 10.3141/2198-09

 Web of Science ®Google Scholar

Várhelyi, A. (1998). Driver's speed behaviour at a zebra crossing: a case study. Accident Analysis & Prevention, 30(6), 731–743. doi: 10.1016/S0001-4575(98)00026-8

 PubMed Web of Science ®Google Scholar

Chin, H. C., Quek, S. T., & Cheu, R. L. (1992). Quantitative examination of traffic conflicts. Transportation Research Record, 1376, 86–91.

 Google Scholar