Abstract
The influence of several traffic parameters including the traffic hourly volume, average speed, and speed variance on the frequency and rate of the crash has been investigated by many researchers. In addition, the impact of human parameters on the spatial and temporal distribution of crashes has also been taken into account. However, the importance of traffic parameters on the temporal distribution of crashes has not been fully analyzed. Therefore, the main aim of this study was to investigate the effects of traffic parameters on the temporal distribution of crashes. Here, the spider plot was utilized for temporal analysis along with linear regression to estimate the impact of traffic hourly volume on the crash ratio. Also, the relationship between the crash’s time and traffic parameters was determined using the Chi-square test. Accordingly, the results indicated a significant impact of traffic hourly volume on the crash ratio. Furthermore, it was observed that some traffic parameters have a significant impact on the temporal distribution of crash frequency and ratio and there are different temporal distribution patterns for different values of traffic parameters.
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References
Abdel-Aty MA, Pemmanaboina R (2006) Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data. IEEE Transactions on Intelligent Transportation Systems 7(2):167–174, DOI: https://doi.org/10.1109/TITS.2006.874710
Belmont D, Forbes T (1953) Effect of average speed and volume on motor-vehicle accidents on two-lane tangents. Proceedings of the thirty-second annual meeting of the highway research board, January 13–16, Washington, DC, USA
Boroujerdian AM, Karimi A, Seyedabrishami S (2014) Identification of hazardous situations using Kernel density estimation method based on time to collision, case study: Left-turn on unsignalized intersection. International Journal of Transportation Engineering 1(4):223–240, DOI: https://doi.org/10.22119/ijte.2014.6296
Castillo-Manzano JI, Castro-Nuño M, Fageda X (2015) Can cars and trucks coexist peacefully on highways? Analyzing the effectiveness of road safety policies in Europe. Accident Analysis & Prevention 77:120–126, DOI: https://doi.org/10.1016/j.aap.2015.01.010
Ceder A, Livneh M (1982) Relationships between road accidents and hourly traffic flow — I: Analyses and interpretation. Accident Analysis & Prevention 14(1):19–34, DOI: https://doi.org/10.1016/0001-4575(82)90004-5
Chen F, Chen S (2011) Injury severities of truck drivers in single-and multi-vehicle accidents on rural highways. Accident Analysis & Prevention 43(5):1677–1688, DOI: https://doi.org/10.1016/j.aap.2011.03.026
Chen F, Chen S, Ma X (2018) Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. Journal of Safety Research 65:153–159, DOI: https://doi.org/10.1016/j.jsr.2018.02.010
Chen F, Song M, Ma X (2019) Investigation on the injury severity of drivers in rear-end collisions between cars using a random parameters bivariate ordered probit model. International Journal of Environmental Research and Public Health 16(14):2632, DOI: https://doi.org/10.3390/ijerph16142632
Da Costa S, Qu X, Parajuli PM (2015) A crash severity-based black spot identification model. Journal of Transportation Safety & Security 7(3):268–277, DOI: https://doi.org/10.1080/19439962.2014.911230
Gargoum SA, El-Basyouny K (2016) Exploring the association between speed and safety: A path analysis approach. Accident Analysis & Prevention 93:32–40, DOI: https://doi.org/10.1016/j.aap.2016.04.029
Hashimoto S, Yoshiki S, Saeki R, Mimura Y, Ando R, Nanba S (2016) Development and application of traffic accident density estimation models using kernel density estimation. Journal of Traffic and Transportation Engineering 3(3):262–270, DOI: https://doi.org/10.1016/j.jtte.2016.01.005
Hossain M, Muromachi Y (2012) A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways. Accident Analysis & Prevention 45:373–381, DOI: https://doi.org/10.1016/j.aap.2011.08.004
Kaygisiz Ö, Düzgün Ş, Yildiz A, Senbil M (2015) Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway. Transportation Research Part F: Traffic Psychology and Behaviour 33:128–140, DOI: https://doi.org/10.1016/j.trf.2015.07.002
Kuo P-F, Zeng X, Lord D (2011) Guidelines for choosing hot-spot analysis tools based on data characteristics, network restrictions, and time distributions. Proceedings of the 91 annual meeting of the transportation research board, January 22–26, Washington, DC, USA
Li L, Zhu L, Sui DZ (2007) A GIS-based Bayesian approach for analyzing spatial-temporal patterns of intra-city motor vehicle crashes. Journal of Transport Geography 15(4):274–285, DOI: https://doi.org/10.1016/jjtrangeo.2006.08.005
Mohaymany AS, Kashani AT, Shahri M (2015) Evaluation of overtaking manoeuvres on two-lane rural roads. Proceedings of the Institution of Civil Engineers-Transport 168(6):523–531, DOI: https://doi.org/10.1680/jtran.13.00066
Plug C, Xia JC, Caulfield C (2011) Spatial and temporal visualisation techniques for crash analysis. Accident Analysis & Prevention 43(6): 1937–1946, DOI: https://doi.org/10.1016/j.aap.2011.05.007
Ponnaluri RV (2016) The odds of wrong-way crashes and resulting fatalities: A comprehensive analysis. Accident Analysis & Prevention 88:105–116, DOI: https://doi.org/10.1016/j.aap.2015.12.012
Ponnaluri RV (2017) Modeling wrong-way crashes and fatalities on arterials and freeways. IATSS Research 42(1):8–17, DOI: https://doi.org/10.1016/j.iatssr.2017.04.001
Qu X, Kuang Y, Oh E, Jin S (2014) Safety evaluation for expressways: A comparative study for macroscopic and microscopic indicators. Traffic Injury Prevention 15(1):89–93, DOI: https://doi.org/10.1080/15389588.2013.782400
Qu X, Meng Q (2014) A note on hotspot identification for urban expressways. Safety Science 66:87–91, DOI: https://doi.org/10.1016/j.ssci.2014.02.006
Quddus M (2013) Exploring the relationship between average speed, speed variation, and accident rates using spatial statistical models and GIS. Journal of Transportation Safety & Security 5(1):27–45, DOI: https://doi.org/10.1080/19439962.2012.705232
Shi Q, Abdel-Aty M (2015) Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transportation Research Part C: Emerging Technologies 58:380–394, DOI: https://doi.org/10.1016/j.trc.2015.02.022
Shirmohammadi H, Hadadi F, Saeedian M (2019) Clustering analysis of drivers based on behavioral characteristics regarding road safety. International Journal of Civil Engineering 17(8):1–14, DOI: https://doi.org/10.1007/s40999-018-00390-2
Spiegelman C, Park ES, Rilett LR (2010) Transportation statistics and microsimulation. CRC Press, Boca Raton, FL, USA
Tavakoli Kashani A, Sokouni Ravasani M, Ayazi E (2016) Analysis of drivers’ behavior using manchester driver behavior questionnaire based on roadside interview in Iran. International Journal of Transportation Engineering 4(1):61–74, DOI: https://doi.org/10.22119/ijte.2016.33497
Theofilatos A, Yannis G (2014) A review of the effect of traffic and weather characteristics on road safety. Accident Analysis & Prevention 72:244–256, DOI: https://doi.org/10.1016/j.aap.2014.06.017
Toran Pour A, Moridpour S, Tay R, Rajabifard A (2017) Influence of pedestrians’ age and gender types on spatial and temporal distribution of pedestrian crashes. Traffic Injury Prevention, DOI: https://doi.org/10.1080/15389588.2017.1341630
Vemulapalli SS, Ulak MB, Ozguven EE, Sando T, Horner MW, Abdelrazig Y, Moses R (2016) GIS-based spatial and temporal analysis of aging-involved accidents: A case study of three counties in Florida. Applied Spatial Analysis and Policy 10(4):537–563, DOI: https://doi.org/10.1007/s12061-016-9192-4
Wu Y, Abdel-Aty M, Lee J (2017) Crash risk analysis during fog conditions using real-time traffic data. Accident Analysis & Prevention, DOI: https://doi.org/10.1016/j.aap.2017.05.004
Xia JC (2011) Data mining of driver characteristics to spatial and temporal hotspots of single vehicle crashes in Western Australia. Proceedings of 19th international congress on modelling and simulation, December 12–16, Perth, Australia
Zeng Q, Wen H, Huang H, Pei X, Wong S (2018) Incorporating temporal correlation into a multivariate random parameters Tobit model for modeling crash rate by injury severity. Transportmetrica A: Transport Science 14(3):177–191, DOI: https://doi.org/10.1080/23249935.2017.1353556
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Kashani, A.T., Zandi, K. Influence of Traffic Parameters on the Temporal Distribution of Crashes. KSCE J Civ Eng 24, 954–961 (2020). https://doi.org/10.1007/s12205-020-0912-6
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DOI: https://doi.org/10.1007/s12205-020-0912-6