Driving Characteristics and Speed Behaviour Parameters of Direct Traffic at Intersections Based on Field Driving Tests

Shijia LI; Yansong LONG; Yanyong GUO; Fanxing KONG; Jin XU

doi:10.7307/ptt.v36i6.587

Authors

Shijia LI Chongqing Jiaotong University, College of Traffic and Transportation
Yansong LONG Chengdu Communications Investment Intelligent Transportation Technology Service Co., Ltd
Yanyong GUO Southeast University, School of Transportation
Fanxing KONG China Railway Eryuan Engineering Group Co., Ltd
Jin XU Chongqing Jiaotong University, College of Traffic and Transportation; Xinjiang Agricultural University, School of Transportation and Logistics Engineering

DOI:

https://doi.org/10.7307/ptt.v36i6.587

Keywords:

traffic engineering, signalised intersection, field driving test, speed characteristics, longitudinal acceleration rate characteristics, simulation verification

Abstract

To reveal the speed control behaviour and manoeuvring characteristics of direct vehicles that stop-go through signalised intersections, a large-scale field driving test was carried out in Chongqing to collect vehicle data under natural driving conditions. The characteristics of speed, longitudinal acceleration rate and their two-dimensional correlation were analysed for deceleration and acceleration behaviour at signalised intersections. Further, a sensitivity analysis of the simulation model on measured data was done with the micro-traffic simulation experiment of a signalised intersection. The following were observed: (1) Drivers’ speed-selection behaviours become more concentrated with closer distance from the stop point. The transects ±25 m from the stop point are abrupt change points in the discrete nature of driver speed-selective behaviours. (2) Drivers’ desire to decelerate during the stop-go through signalised intersections is more robust, with the magnitude of pedal manoeuvres for deceleration behaviours being more intense than that for acceleration behaviours. (3) There is a nonlinear correlation between longitudinal acceleration rate and speed. The longitudinal acceleration rate increased with increase in speed and then decreased with the inflection point at 15 km/h. (4) The micro-traffic simulation’s acceleration rate model is sensitive to measured acceleration rate parameters. This study guides the parameter setting of speed, deceleration rate and acceleration rate models for microscopic traffic simulation and for parameter calibration of the car-following model.

References

Hua J, et al. Modeling and simulation of approaching behaviors to signalized intersections based on risk quantification. Transportation Research Part C: Emerging Technologies. 2022;142:103773. DOI: 10.1016/j.trc.2022.103773.

Wu Z, et al. Study on the collision avoidance strategy at unsignalized intersection based on PreScan simulation. Procedia-social and Behavioral Sciences. 2013;96:1315–1321. DOI: 10.1016/j.sbspro.2013.08.149.

Al-Ghamdi AS. Analysis of traffic accidents at urban intersections in Riyadh. Accident Analysis & Prevention. 2003;35(5):717–724. DOI: 10.1016/S0001-4575(02)00050-7.

Malaghan V, et al. Modeling acceleration and deceleration rates for two-lane rural highways using Global Positioning System data. Journal of Advanced Transportation. 2021;2021:1–17. DOI: 10.1155/2021/6630876.

Liu CH, et al. Learning the driver acceleration/deceleration behavior under high-speed environments from naturalistic driving data. IEEE Intelligent Transportation Systems Magazine. 2020;14(3):78–91. DOI: 10.1109/MITS.2020.3014115.

Zhang Y, et al. A new car-following model considering driving characteristics and preceding vehicle’s acceleration. Journal of Advanced Transportation. 2017;2017. DOI: 10.1155/2017/2437539.

Wang F, et al. Investigating drivers’ decision zones at high-speed intersections in China based on the acceleration-deceleration diagram. Case Studies on Transport Policy. 2020;8(1):112–118. DOI: 10.1016/j.cstp.2018.06.006.

Choi EJ, et al. Critical aggressive acceleration values and models for fuel consumption when starting and driving a passenger car running on LPG. International Journal of Sustainable Transportation. 2017;11(6):395–405. DOI: 10.1080/15568318.2016.1262928.

Tan W, et al. Modeling the effects of speed limit, acceleration, and deceleration on overall delay and traffic emission at a signalized intersection. Journal of Transportation Engineering Part A: Systems. 2017;143(12):04017063. DOI: 10.1061/JTEPBS.0000101.

Zhang YL, et al. Evaluation of vehicle acceleration models for emission estimation at an intersection. Transportation Research Part D: Transport and Environment. 2013;18:46–50. DOI: 10.1016/j.trd.2012.09.004.

Bogdanovic V, et al. The research of vehicle acceleration at signalized intersections. Promet-Traffic&Transportation. 2013;25(1):33–42. DOI: 10.7307/ptt.v25i1.1245.

Xia LH, et al. Longitudinal driving behavior before, during, and after a left-turn movement at signalized intersections: a naturalistic driving study in China. Sustainability. 2022;14(18):11630. DOI: 10.3390/su141811630

Jiang ZH, et al. Driver responses to green signal flash and countdown displays at signalized intersections: a comparative study using driving simulator. International Conference on Transportation and Development 2018: Connected and Autonomous Vehicles and Transportation Safety, 15-18 Jul. 2018, Pittsburgh, PA, USA. 2018. p. 275-284. https://www.scimagojr.com/journalsearch.php?q=21100870899&tip=sid&clean=0.

Wortman RH, et al. An evaluation of vehicle deceleration profiles. Journal of Advanced Transportation. 1994;28(3):203–215. DOI: 10.1002/atr.5670280303.

Feng RK, et al. Vehicle deceleration characteristics of intersection based on driver's physiological reaction. Journal of Beijing University of Technology. 2019;45(07):679–684. DOI: 10.11936/bjutxb2017120037.

Wang J, et al. Normal deceleration behavior of passenger vehicles at stop sign–controlled intersections evaluated with in-vehicle global positioning system data. Transportation Research Record. 2005;1937(1):120–127. DOI: 10.3141/1937-17.

Da Lio M, et al. Biologically guided driver modeling: the stop behavior of human car drivers. IEEE Transactions on Intelligent Transportation Systems. 2017;19(8):2454–2469. DOI: 10.1109/TITS.2017.2751526.

Almallah M, et al. Empirical evaluation of drivers’ start-up behavior at signalized intersection using driving simulator. Procedia Computer Science. 2020;170:227–234. DOI: 10.1016/j.procs.2020.03.034.

Scanlon JM, et al. Models of driver acceleration behavior prior to real-world intersection crashes. IEEE Transactions on Intelligent Transportation Systems. 2018;19(3):774–786. DOI: 10.1109/TITS.2017.2699079.

Boonsiripant S, et al. Measurement and comparison of acceleration and deceleration zones at traffic control intersections. Transportation Research Record. 2010;2171(1):1–10. DOI: 10.3141/2171-01.

Bai JR, et al. The effect analysis of longitudinal deceleration line on urban road intersection in mountainous area. Science Technology and Engineering. 2020;20(17):7040–7045.

Singh MK, et al. Driver behavior modelling of vehicles at signalized intersection with heterogeneous traffic. IATSS Research. 2022;46(2):236–246. DOI: 10.1016/j.iatssr.2021.12.008.

Helbing D, Tilch B. Generalized force model of traffic dynamics. Physical Review E. 1998;58(1):133–138. DOI: 10.1103/PhysRevE.58.133.

Aycin MF, et al. Stability and performance of car-following models in congested traffic. Journal of Transportation Engineering. 2001;27(1):2–12. DOI: 10.1061/(ASCE)0733-947X(2001)127:1(2).

Zhao H, et al. An extended car-following model at signalised intersections. Journal of Advanced Transportation. 2018;2018(PT.4):5427507.1–5427507.26. DOI: 10.1155/2018/5427507.

Zhang J, et al. Some features of car‐following behavior in the vicinity of signalised intersection and how to model them. IET Intelligent Transport Systems. 2019;13(11):1686–1693. DOI: 10.1049/iet-its.2018.5510.

Pathivada BK, et al. Investigating dilemma zone boundaries for mixed traffic conditions using support vector machines. Transportation Letters-The International Journal of Transportation Research. 2022;14(4):378–384. DOI: 10.1080/19427867.2020.1870307.

Köll H, et al. Driver behavior during flashing green before amber: A comparative study. Accident Analysis & Prevention. 2004;36(2):273–280. DOI: 10.1016/S0001-4575(03)00005-8.

Long KJ, et al. Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections: An empirical study in Changsha, China. Safety Science. 2013;54:8–16. DOI: 10.1016/j.ssci.2012.10.007.

Li ZX, et al. Modeling dynamics of dilemma zones by formulating dynamical contributing factors with video-observed trajectory data. Procedia-Social and Behavioral Sciences. 2013;80:880–900. DOI: 10.1016/j.sbspro.2013.05.048.

Papaioannou P. Driver behavior, dilemma zone and safety effects at urban signalised intersections in Greece. Accident Analysis & Prevention. 2007;39(1):147–158. DOI: 10.1016/j.aap.2006.06.014.

Rakha H, et al. Modeling driver behavior within a signalized intersection approach decision-dilemma zone. Transportation Research Record Journal of the Transportation Research Board. 2008;44(2069):16–25. DOI: 10.3141/2069-03.

Zhang HL, et al. Effect factors analyzing on the driver choices behavior on the dilemma zone at the signalized intersections based on structural equation model. Science Technology and Engineering. 2018;18(28): 254–259.

Majhi RC, et al. Analyzing driver's response to yellow indication subjected to dilemma incursion under mixed traffic condition. Journal of Traffic and Transportation Engineering (English Edition). 2021;8(1):107–116. DOI: 10.1016/j.jtte.2019.05.005.

Wang F, et al. Modeling risky driver behavior under the influence of flashing green signal with vehicle trajectory data. Transportation Research Record. 2016;2562(1):53–62. DOI: 10.3141/2562-07.

Chauhan R, et al. Analysing driver’s decision in dilemma zone at signalized intersections under disordered traffic conditions. Transportation Research Part F: Traffic Psychology and Behavior. 2022;89:222–235. DOI: 10.1016/j.trf.2022.06.016.

Lee S, et al. Driving characteristics analysis method based on real-world driving data. Energies. 2024;17(1):185. DOI: 10.3390/en17010185.

Xu J, et al. Speed behavior and mental workload of small-spacing expressway interchanges based on field driving test. Ergonomics. 2023;1–18. DOI: 10.1080/00140139.2023.2278395.

Zhou AY, et al. Car-following behavior of human-driven vehicles in mixed-flow traffic: a driving simulator study. IEEE Transactions on Intelligent Vehicles. 2023;8(4):2661–2673. DOI: 10.1109/TIV.2023.3257962.

Xu D, et al. Research on driver's speed control behavior at urban signalized intersection. Proceedings of the International Workshop on Materials Engineering and Computer Sciences, 2018, 27-28 Jan. 2018, Jinan, Shandong, China. 2018. p. 126–129. https://www.atlantis-press.com/proceedings/iwmecs-18.

Zhou CJ, et al. Parameter sensitivity analysis method for microscopic traffic simulation experiment. Journal of BEIJING University of Technology. 2016;42(11):1728–1733. DOI: 10.11936/bjutxb2015070088.

Ahn K, et al. Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. Journal of Transportation Engineering. 2002;128(2):182–190. DOI: 10.1061/(ASCE)0733-947X(2002)128:2(182).

Moon S, et al. Human driving data-based design of a vehicle adaptive cruise control algorithm. Vehicle System Dynamics. 2008;46(8):661–690. DOI: 10.1080/00423110701576130.