Journal articles: 'Car-following'

1

Salmon, Vincent. "Safe Car Following." SIAM Review 29, no. 3 (September 1987): 470. http://dx.doi.org/10.1137/1029081.

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2

Ranney, Thomas A. "Psychological factors that influence car-following and car-following model development." Transportation Research Part F: Traffic Psychology and Behaviour 2, no. 4 (December 1999): 213–19. http://dx.doi.org/10.1016/s1369-8478(00)00010-3.

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3

Mika, Péter. "Adaptive Car Following Model." Strojnícky casopis – Journal of Mechanical Engineering 68, no. 3 (November 1, 2018): 281–88. http://dx.doi.org/10.2478/scjme-2018-0041.

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AbstractThe vehicle dynamics was a very lot developed in the last twenty years. There is a huge gap in the different vehicle models in this field. Researchers need accurate the car following models because of it. There are several mathematical model, which describe the dynamics and the motion of individual vehicles. This models based on a desired velocity, which is kept by the following vehicle and even small gaps will not induce braking reactions. So this behaviour is not realistic.

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4

Wang, Yanbing, Maria Laura Delle Monache, and Daniel B. Work. "Identifiability of car-following dynamics." Physica D: Nonlinear Phenomena 430 (February 2022): 133090. http://dx.doi.org/10.1016/j.physd.2021.133090.

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5

Deng, Hui, and H. Michael Zhang. "Driver Anticipation in Car Following." Transportation Research Record: Journal of the Transportation Research Board 2316, no. 1 (January 2012): 31–37. http://dx.doi.org/10.3141/2316-04.

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6

Panwai, Sakda, and Hussein Dia. "Neural Agent Car-Following Models." IEEE Transactions on Intelligent Transportation Systems 8, no. 1 (March 2007): 60–70. http://dx.doi.org/10.1109/tits.2006.884616.

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7

Ma, Dongfang, Yueyi Han, and Sheng Jin. "Solid angle car following model." Chinese Physics B 29, no. 6 (June 2020): 060504. http://dx.doi.org/10.1088/1674-1056/ab862c.

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8

Ossen, Saskia, Serge P. Hoogendoorn, and Ben G. H. Gorte. "Interdriver Differences in Car-Following." Transportation Research Record: Journal of the Transportation Research Board 1965, no. 1 (January 2006): 121–29. http://dx.doi.org/10.1177/0361198106196500113.

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9

Brackstone, Mark, and Mike McDonald. "Car-following: a historical review." Transportation Research Part F: Traffic Psychology and Behaviour 2, no. 4 (December 1999): 181–96. http://dx.doi.org/10.1016/s1369-8478(00)00005-x.

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10

Kendziorra, Andreas, Peter Wagner, and Tomer Toledo. "A Stochastic Car Following Model." Transportation Research Procedia 15 (2016): 198–207. http://dx.doi.org/10.1016/j.trpro.2016.06.017.

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11

Wagner, Peter. "Analyzing fluctuations in car-following." Transportation Research Part B: Methodological 46, no. 10 (December 2012): 1384–92. http://dx.doi.org/10.1016/j.trb.2012.06.007.

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12

Farhi, Nadir. "Piecewise linear car-following modeling." Transportation Research Part C: Emerging Technologies 25 (December 2012): 100–112. http://dx.doi.org/10.1016/j.trc.2012.05.005.

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13

Pinault, Steven C. "Safe Car Following (Vincent Salmon)." SIAM Review 30, no. 3 (September 1988): 507–8. http://dx.doi.org/10.1137/1030106.

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14

Lenz, H., C. K. Wagner, and R. Sollacher. "Multi-anticipative car-following model." European Physical Journal B 7, no. 2 (January 1999): 331–35. http://dx.doi.org/10.1007/s100510050618.

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15

Shi, Yun-Feng, Shen-Xue Hao, Qian Liu, and Li-Cai Yang. "Clustered car-following strategy for improving car-following stability under Cooperative Vehicles Infrastructure Systems." IET Intelligent Transport Systems 10, no. 3 (April 1, 2016): 141–47. http://dx.doi.org/10.1049/iet-its.2015.0019.

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16

WENG, Yan-lin. "Car-following models of vehicular traffic." Journal of Zhejiang University SCIENCE 3, no. 4 (2002): 412. http://dx.doi.org/10.1631/jzus.2002.0412.

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17

Markov, A. E., S. E. Terekhov, A. E. Andreev, and R. E. Gorelik. "TRAJECTORY FOLLOWING SYSTEM FOR AUTONOMOUS CAR." IZVESTIA VOLGOGRAD STATE TECHNICAL UNIVERSITY, no. 9(244) (September 25, 2020): 52–56. http://dx.doi.org/10.35211/1990-5297-2020-9-244-52-56.

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18

Wang, Zejiang, Xingyu Zhou, and Junmin Wang. "Algebraic Car-Following Model Parameter Identification." IFAC-PapersOnLine 54, no. 20 (2021): 864–69. http://dx.doi.org/10.1016/j.ifacol.2021.11.280.

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19

van Hinsbergen, Chris P. IJ, Hans W. C. van Lint, Serge P. Hoogendoorn, and Henk J. van Zuylen. "Bayesian Calibration of Car-Following Models." IFAC Proceedings Volumes 42, no. 15 (2009): 91–97. http://dx.doi.org/10.3182/20090902-3-us-2007.0049.

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20

Farhi, Nadir, Habib Haj-Salem, and Jean-Patrick Lebacque. "Multianticipative Piecewise-Linear Car-Following Model." Transportation Research Record: Journal of the Transportation Research Board 2315, no. 1 (January 2012): 100–109. http://dx.doi.org/10.3141/2315-11.

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21

Crundall, David, Claire Shenton, and Geoffrey Underwood. "Eye Movements during Intentional Car following." Perception 33, no. 8 (August 2004): 975–86. http://dx.doi.org/10.1068/p5105.

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22

Berg, Peter, Anthony Mason, and Andrew Woods. "Continuum approach to car-following models." Physical Review E 61, no. 2 (February 1, 2000): 1056–66. http://dx.doi.org/10.1103/physreve.61.1056.

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23

Mulder, Mark, Jasper J. A. Pauwelussen, Marinus M. van Paassen, Max Mulder, and David A. Abbink. "Active Deceleration Support in Car Following." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 40, no. 6 (November 2010): 1271–84. http://dx.doi.org/10.1109/tsmca.2010.2044998.

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24

王, 秀娟. "Multistability in Delay Car-Following Models." Dynamical Systems and Control 08, no. 04 (2019): 278–84. http://dx.doi.org/10.12677/dsc.2019.84030.

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25

Wang, Jiao, Ronghui Liu, and Frank Montgomery. "Car-Following Model for Motorway Traffic." Transportation Research Record: Journal of the Transportation Research Board 1934, no. 1 (January 2005): 33–42. http://dx.doi.org/10.1177/0361198105193400104.

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This paper presents a new car-following model that aims to capture some of the key motorway flow characteristics, namely, traffic breakdown, hysteresis, and shock wave propagation, as well as close-following behavior. The model proposes three different driving states: nonalert, alert, and close following. Under the different driving states, drivers apply different reaction times and accelerations. This paper presents the formulation and algorithmic implementation of the model. The theoretical analysis of the macroscopic flow–density relationships of the model is discussed. Simulation experiments were conducted, and the results are examined at both the macroscopic level (speed breakdown and traffic hysteresis) and the microscopic level (gap distribution and shock wave propagation). The results show that the model is able to capture realistically the speed drop, traffic hysteresis, and shock wave propagation as well as close-following behavior. Further studies of the sensitivities of key model parameters suggest that the drivers’ reaction times have a significant effect on the modeled capacity and occupancy, while the effect of the speed threshold that distinguishes congested from noncongested traffic flow is less significant.

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26

Hoogendoorn, Serge P., Saskia Ossen, and M. Schreuder. "Empirics of Multianticipative Car-Following Behavior." Transportation Research Record: Journal of the Transportation Research Board 1965, no. 1 (January 2006): 112–20. http://dx.doi.org/10.1177/0361198106196500112.

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27

Boer, Erwin R. "Car following from the driver’s perspective." Transportation Research Part F: Traffic Psychology and Behaviour 2, no. 4 (December 1999): 201–6. http://dx.doi.org/10.1016/s1369-8478(00)00007-3.

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28

Gunay, Banihan. "Car following theory with lateral discomfort." Transportation Research Part B: Methodological 41, no. 7 (August 2007): 722–35. http://dx.doi.org/10.1016/j.trb.2007.02.002.

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29

Papathanasopoulou, Vasileia, and Constantinos Antoniou. "Towards data-driven car-following models." Transportation Research Part C: Emerging Technologies 55 (June 2015): 496–509. http://dx.doi.org/10.1016/j.trc.2015.02.016.

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30

Xu, Xihua, John Pang, and Christopher Monterola. "Asymmetric optimal-velocity car-following model." Physica A: Statistical Mechanics and its Applications 436 (October 2015): 565–71. http://dx.doi.org/10.1016/j.physa.2015.04.023.

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31

Addison, Paul S., and David J. Low. "A novel nonlinear car-following model." Chaos: An Interdisciplinary Journal of Nonlinear Science 8, no. 4 (December 1998): 791–99. http://dx.doi.org/10.1063/1.166364.

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32

McCartney, M. "Follow that car! Investigating a simple class of car following model." Teaching Mathematics and its Applications 19, no. 2 (June 1, 2000): 83–87. http://dx.doi.org/10.1093/teamat/19.2.83.

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33

Shi, Y. F., and L. C. Yang. "Improved coupled map car-following model considering partial car-to-car communication and its jam analysis." Canadian Journal of Physics 95, no. 11 (November 2017): 1096–102. http://dx.doi.org/10.1139/cjp-2016-0639.

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The characteristics and the nonlinear phenomenon of traffic flow in the case of car-to-car communication (C2CC) are studied based on an improved coupled map car-following model. The model incorporates the modified optimal velocity function and appropriate control method. The conditions necessary to maintain the system stability and suppress traffic jams are obtained. To describe the car-following dynamics under C2CC accurately, different penetration rates of C2CC vehicles, such as 10%, 30%, and 60% are considered. The simulation results suggest that the improved model can effectively suppress traffic jams. The extent to which traffic jams are suppressed is increasing as the penetration rate increases. Moreover, the car-following stability has a noticeable improvement by analysing the time–space plots.

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34

Tu, Chunling, and Shengzhi Du. "Hybrid order characteristics in car-following behavior." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (October 1, 2020): 158. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp158-166.

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<span>This paper addresses the discovery of an interesting property in car-following processes, which was not reported in the existing literatures. A hybrid order behavior is supported by both experimental data and theoretical simulations. To demonstrate this behavior, the first order and the second order car-following behaviors are defined. Then, by comparing the first and the second order car-following behaviors in the existing analystic models and the real traffic context, this paper finds that a significant amount of the second order car-following processes in real traffic context do not match the existing models and structural mismatches are observed. The popularity and significance of such cases suggest the existence of unmodelled dynamics in the existing methods, that is, the car following behavior should be determined by more factors than the immediate proceeding vehicle. Therefore, the existing car-following models must be improved to accommodate these factors. This forms one of the main values of this paper. This paper then defines the hybrid order car-following behavior and prompts to associate this behavior with the concerned unmodelled dynamics (mismatches between the actual traffic data and the simulation from models). The neural network is employed to model such dynamics. The idea of the proposed hybrid order behavior matches the fact that the car-following behavior is determined by multiple vehicles driving in front of the subject car instead of only the immediate proceeding one. This is valuable because it provides guidance on the improvement of existing car-following models. The neural network model validates that the consideration of multiple vehicles improves the accuracy of car-following modelling.</span>

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35

Zhao, Hongxing, Ruichun He, and Changxi Ma. "An Extended Car-Following Model at Signalised Intersections." Journal of Advanced Transportation 2018 (July 25, 2018): 1–26. http://dx.doi.org/10.1155/2018/5427507.

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An extended car-following model is proposed on the basis of experimental analysis to improve the performance of the traditional car-following model and simulate a microscopic car-following behaviour at signalised intersections. The new car-following model considers vehicle gather and dissipation. Firstly, the parameters of optimal velocity, generalised force and full velocity difference models are calibrated by measured data, and the problems and causes of the three models are analysed with a realistic trajectory simulation as an evaluation criterion. Secondly, an extended car-following model based on the full optimal velocity model is proposed by considering the vehicle gather and dissipation. The parameters of the new car-following model are calibrated by the measured data, and the model is compared with comparative models on the basis of isolated point data and the entire car-following process. Simulation results show that the optimal velocity, generalised force, and full velocity difference models cannot effectively simulate a microscopic car-following behaviour at signalised intersections, whereas the new car-following model can avoid a collision and has a high fit degree for simulating the measured data of the car-following behaviour at signalised intersections.

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36

Song, Cheng-Ju, and Hong-Fei Jia. "Car-Following Model Optimization and Simulation Based on Cooperative Adaptive Cruise Control." Sustainability 14, no. 21 (October 28, 2022): 14067. http://dx.doi.org/10.3390/su142114067.

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This study aims to improve the desired distance adaptability of the cooperative adaptive cruise control (CACC) during car-following. In this study, the characteristics of the desired distance in different traffic flow states were analyzed. The general functional form of the desired distance in the car-following process was formulated. Then, a car-following platoon was constructed to compare the car-following effect of the platoon under different conditions, using the following speed and the lead vehicle disturbance, as the observed variable and the simulation condition, respectively. The car-following effect of the platoon under different parameters was also compared, based on the improved CACC model. The results show that the improved CACC model exhibited more advantages in car-following efficiency, it can better describe the state of the car-following queue under different traffic flow parameters and car-following behavior conditions, it has a strong anti-interference ability for the fluctuation of the car-following queue and is conducive to further improving the intelligent operation of car-following queue.

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37

Hu, Jie, and Sheng Luo. "A Car-Following Driver Model Capable of Retaining Naturalistic Driving Styles." Journal of Advanced Transportation 2020 (January 21, 2020): 1–16. http://dx.doi.org/10.1155/2020/6520861.

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The modeling of car-following behavior is an attractive research topic in traffic simulation and intelligent transportation. The driver plays an important role in car following but is ignored by most car-following models. This paper presents a novel car-following driver model, which can retain aspects of human driving styles. First, simulated car-following data are generated by using the speed control driver model and the real-world driving behavior data if the real-world car-following data are not available. Then, the car-following driver model is established by imitating human driving maneuver during real-world car following. This is accomplished by using a neural network-based learning control paradigm and car-following data. Finally, the FTP-72 driving cycle is borrowed as the speed profile of the leading vehicle for the model test. The driving style is quantitatively analyzed by AESD. The results show that the proposed car-following driver model is capable of retaining the naturalistic driving styles while well accomplishing the car-following task with the error of relative distance mostly less than 5 meters for every driving styles.

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38

Zhaohui Wu, Yanfei Liu, and Gang Pan. "A Smart Car Control Model for Brake Comfort Based on Car Following." IEEE Transactions on Intelligent Transportation Systems 10, no. 1 (March 2009): 42–46. http://dx.doi.org/10.1109/tits.2008.2006777.

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39

Zhang, Junjie, Can Yang, Jun Zhang, and Haojie Ji. "Effect of Five Driver’s Behavior Characteristics on Car-Following Safety." International Journal of Environmental Research and Public Health 20, no. 1 (December 21, 2022): 76. http://dx.doi.org/10.3390/ijerph20010076.

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Driver’s behavior characteristics (DBCs) influence car-following safety. Therefore, this paper aimed to analyze the effect of different DBCs on the car-following safety based on the desired safety margin (DSM) car-following model, which includes five DBC parameters. Based on the Monte Carlo simulation method, the effect of DBCs on car-following safety is investigated under a given rear-end collision (RECs) condition. We find that larger subjective risk perception levels can reduce RECs, a smaller acceleration sensitivity (or a larger deceleration sensitivity) can improve car-following safety, and a faster reaction ability of the driver can avoid RECs in the car-following process. It implies that DBCs would cause a traffic wave in the car-following process. Therefore, a reasonable value of DBCs can enhance traffic flow stability, and a traffic control strategy can improve car-following safety by using the adjustment of DBCs.

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40

Dijker, Thomas, Piet H. L. Bovy, and Raymond G. M. M. Vermijs. "Car-Following Under Congested Conditions: Empirical Findings." Transportation Research Record: Journal of the Transportation Research Board 1644, no. 1 (January 1998): 20–28. http://dx.doi.org/10.3141/1644-03.

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In traffic flow analysis several regimes are distinguished, such as congested and noncongested flow conditions. Indications exist that driving behavior differs by regime and that it may change discontinuously between regimes. In contrast most traffic flow models used today basically assume the same car-following behavior irrespective of the traffic flow regime. It is hypothesized that, because of this deficiency, these models do not always perform satisfactorily. To clarify this issue, differences in car-following between congested and noncongested flow are analyzed with data from two sites on Dutch freeways. It is shown that, at the same speeds, passenger car drivers follow with smaller headways in noncongested than in congested flow. Car-following of truck drivers does not show differences between regimes. Microscopic distance gap-speed models are established for several road-user classes, valid for each of the two flow regimes. To show the improvements resulting from these new microscopic relationships, the latter are implemented in a microscopic simulation model with which macroscopic patterns in traffic flow are modeled. The macroscopic findings produced with the regime-specific car-following rules show a considerable improvement in modeling performance.

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41

Shu, Xiaolin, Shuo Jin, Ying Zhang, Hongbo Zhou, and Guanghong Lu. "The Physical Foundation of Car-following Model." Creative Education 03, no. 07 (2012): 114–16. http://dx.doi.org/10.4236/ce.2012.37b030.

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42

Rakha, Hesham, Caroline Cavagni Pecker, and Helena Beatriz Bettella Cybis. "Calibration Procedure for Gipps Car-Following Model." Transportation Research Record: Journal of the Transportation Research Board 1999, no. 1 (January 2007): 115–27. http://dx.doi.org/10.3141/1999-13.

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43

Rakha, Hesham, and Weidong Wang. "Procedure for Calibrating Gipps Car-Following Model." Transportation Research Record: Journal of the Transportation Research Board 2124, no. 1 (January 2009): 113–24. http://dx.doi.org/10.3141/2124-11.

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44

Ramezani-Khansari, Ehsan, Masoud Tabibi, and Fereidoon Moghadas Nejad. "Validating Driving Simulator for Car-Following Distance." Iranian Journal of Science and Technology, Transactions of Civil Engineering 45, no. 1 (January 16, 2021): 281–90. http://dx.doi.org/10.1007/s40996-020-00576-6.

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45

Lv, Feng, Cai Hong Ye, and Hong Xia Ge. "Anticipation Driving Behavior in Car Following Theory." Applied Mechanics and Materials 505-506 (January 2014): 1133–36. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.1133.

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In this paper, a new anticipation driving car-following model (AD-TVDM) is presented based on the two velocity difference model (TVDM)[1], taking into the effect of anticipation driving behavior in real world. The nature of the model is investigated by using linear and nonlinear analysis method. A thermodynamic theory is formulated to describe the phase transition and critical phenomenon in traffic flow and the time-dependent Ginzburg-Landau (TDGL) equation is derived to describe the traffic flow near the critical point.

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46

Aycin, M. F., and R. F. Benekohal. "Comparison of Car-Following Models for Simulation." Transportation Research Record: Journal of the Transportation Research Board 1678, no. 1 (January 1999): 116–27. http://dx.doi.org/10.3141/1678-15.

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47

Batista, Milan, and Elen Twrdy. "Optimal velocity functions for car-following models." Journal of Zhejiang University-SCIENCE A 11, no. 7 (July 2010): 520–29. http://dx.doi.org/10.1631/jzus.a0900370.

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48

Stépán, Gábor, and Gábor Orosz. "HOPF CALCULATIONS IN DELAYED CAR-FOLLOWING MODELS." IFAC Proceedings Volumes 39, no. 10 (2006): 193–98. http://dx.doi.org/10.3182/20060710-3-it-4901.00032.

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49

Ciuffo, Biagio, Vincenzo Punzo, and Marcello Montanino. "Thirty Years of Gipps’ Car-Following Model." Transportation Research Record: Journal of the Transportation Research Board 2315, no. 1 (January 2012): 89–99. http://dx.doi.org/10.3141/2315-10.

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50

Tang, Tie-Qiao, Jin-Gang Li, Dong Zhang, and Yun-Peng Wang. "Vehicle's exhaust emissions under car-following model." International Journal of Modern Physics C 25, no. 06 (April 23, 2014): 1450007. http://dx.doi.org/10.1142/s0129183114500077.

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In this paper, we explore each vehicle's exhaust emissions under the full velocity difference (FVD) model and the car-following model with consideration of the traffic interruption probability during three typical traffic situations. Numerical results show that the vehicle's exhaust emissions of the second model are less than those of the first model under the three typical traffic situations, which shows that the second model can reduce each vehicle's exhaust emissions.

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Journal articles on the topic 'Car-following'

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