Nonlinear Analysis of a New Car-Following Model Based on Internet-Connected Vehicles

Nonlinear Analysis of a New Car-Following Model Based on Internet-Connected Vehicles Lei Yu1*, Bingchang Zhou, Zhongke Shi1 1 College School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi Province, China of Science, Northwestern Polytechnical University, Xi'an, Shaanxi Province, China * Corresponding author Tel: (+8)153398; email: yuleijk@1com Manuscript submitted July 9, 1; accepted December, 1 doi: 1177/ijcce15v39 Abstract: A new car-following model is proposed based on Internet-connected vehicles In this model, a vehicle is controlled by the information of arbitrary number of vehicles obtained from the Internet-connected vehicles system The stability condition is investigated by using the linear stability theory The result shows that the stability of traffic flow is improved by taking into account the of vehicles ahead and the relative velocity By applying the nonlinear analysis, the modified Korteweg-de Vries (mkdv) equation is derived to describe the traffic jams From the numerical simulation, it is shown that the traffic jams are suppressed efficiently by taking into account the of vehicles ahead and the relative velocity Key words: Traffic flow, Internet-connected vehicles, modified Korteweg-de Vries equation, Kink-antikink soliton, density wave 1 Introduction Traffic flow, especially of traffic jams, is an interesting problem To understand the rich variety of vehicular traffic, many traffic flow models are proposed to describe traffic flow, such as car-following models, cellular automaton (CA) models, gas kinetic models and hydrodynamic models [1]-[19] The advantage of car-following models is that we can easy analyze the analytical structure of the models One car-following model is proposed and analyzed by Newell [] and Whitham [1] The optimal velocity (OV) model, another version of car-following model, is proposed by Bando et al [] without introducing a delay time The OV model successfully describes the dynamical formation of traffic jams and reveals the transition mechanism very simply In the OV model, acceleration of vehicle is described by a simple differential equation using the optimal velocity function, which is depended on the (the distance between two successive vehicles), that is, the driver is supposed to look at the preceding car only In more realistic situation, the driver considers more vehicles around him/her From the viewpoint of control theory for traffic flow, that is important to suppress the formation of traffic congestion So there have been many works to extending the OV model to more realistic traffic model [3]-[5] The Internet-connected vehicles era comes based on the technologies of wireless network communication, GPS and other information technology Vehicles can communicate with each other by vehicle to vehicle (VV) communication and with roadside base stations by vehicle to infrastructure (VI) communication in vehicular network [] Based on the fact that real-time systems of traffic information are becoming widely available, each vehicle can receive information of other vehicles and then decide its 117 Volume, Number, March 15

optimal behavior, the studies have been focused on how to use the information owned to suppress the appearance of traffic congestion efficiently Hasebe et al [7] discuss the property of the forward looking OV model, in which the of arbitrary number of vehicles that precede are considered Ge et al [8]-[9] discuss the stability condition of the model which take into account the of arbitrary number of vehicles ahead and derive the modified Korteweg-de Vries (mkdv) equation and Korteweg-de Vries (KdV) equation respectively Li and Liu [3] introduce relative velocity of arbitrary number of vehicles ahead into the OV model and find that the stability of traffic flow is improved The purpose of this paper is to analyze the Newell- Whitham-type car-following model which considers not only the of arbitrary number of vehicles ahead but also the relative velocity We analyze the effect of the of vehicles ahead and the relative velocity upon the stability of traffic flow by using the linear theory The result shows that the stability of traffic flow is improved by taking into account the of vehicles ahead and the relative velocity Moreover, we apply the nonlinear analysis to derive the mkdv equation near the critical point and obtain its kink-antikink soliton solution to describe traffic jams Finally we carry out the computer simulation for the extended model with periodic boundary condition The numerical simulation is good agreement with the analytic results Model Newell [] and Whitham [1] propose a model which is given by a first-order differential equation by introducing a delay time which plays an important role in the occurrence of traffic congestion The motion of j th car is given as follows dx j (t ) dt (1) V ( x j (t )), x j (t ) x j 1 x j is the between two successive vehicles of the V ( x j (t )) is the optimal velocity function and th vehicle at time, is the delay time According to the idea mentioned above, we extend the Newell-Whitham-type model (1) to take into account the of - cars ahead and the relative velocity Then the motion of th car is described by the following differential equation dx j (t ) dt V ( x j (t ), x j 1 (t ),, x j (t ), v j (t )), is the extended optimal velocity function including variables of the relative velocity () and the of -cars ahead of the th vehicle We assume that the driver can obtain the information of -vehicles that precede based on Internet-connected vehicles We assume that the extended optimal velocity function is V ( x j (t ), x j 1 (t ),, x j n 1 (t ), v j (t )) V ( x j (t ), x j 1 (t ),, x j n 1 (t )) v j (t ), the weighted coefficient of the relative velocity is a constant independent of time, velocity and position Then, the extended model () can be rewritten as dx j (t ) dt V ( x j (t ), x j 1 (t ),, x j (t )) v j (t ), (3) If, Eq(3) is the model which is proposed by Ge et al [8]-[9] We rewrite Eq(3) to obtain the difference equation 118 Volume, Number, March 15

x j (t ) x j (t ) V ( x j (t ), x j 1 (t ),, x j (t )) ( x j (t ) x j (t )) () The optimal velocity function is selected similar to that proposed by Bando et al [] V ( x j (t ), x j 1 (t ),, x j (t )) is the maximal velocity, vmax tanh( l (5) x j l (t ) hc ) tanh(hc ), is the safety distance and is the weighted coefficient of We suppose that have the following properties: 1) decrease monotonically with increasing, that is, The reason is that as the distance between the car ahead and the considered car increases the effect of cars ahead on the car motion reduces gradually ) l for 1, Here we select and for We name l x j l (t ) in Eq (5) as the weighted The optimal velocity function V in Eq (5) is a monotonically increasing function of the weighted and has an upper bound (ie the maximal velocity) When the weighted is less than the safety distance, the vehicle reduces its velocity to prevent from crashing into the vehicle ahead Otherwise, if the weighted is larger than the safety distance, the car velocity increases to the maximal velocity The optimal velocity function V has the turning point (inflection point) at function V l x j l (t ) hc It is important that the optimal velocity has the turning point Otherwise, we cannot derive mkdv equation which has the kink-antikink soliton solution to describe the traffic jams For later convenience, Eq () can be rewritten in terms of the as follows x j (t ) x j (t ) V ( l x j l 1 (t )) V ( l x j l (t )) [ x j 1 (t ) x j 1 (t ) x j (t ) x j (t )] () 3 Linear Stability Analysis We apply the linear stability theory to analyze the extended model described by Eq () The stability of the uniform traffic flow is considered, which is such a state that all cars move with identical h and optimal velocity The solution representing the uniform steady state of Eq () can be written as (7) N is the total number of vehicles and L is the road length Suppose to be the small deviation from the uniform steady state Substituting it into Eq() and linearizing the resulting equation, we can obtain y j (t ) y j (t ) V l ( y j l 1 (t ) y j l (t )) [ y j 1 (t ) y j 1 (t ) y j (t ) y j (t )] (8) 119 Volume, Number, March 15

For simplicity, V ' indicates the derivative of the optimal velocity function at in the above equation and hereafter By expanding, the following equation of (e z 1)[e z (eik 1)] V (eik 1)( l eikl ) is obtained (9) By expanding (ik ) and inserting it into Eq(9), the first- and second-order terms of ik are obtained z1 V '(h), z 3 (V (h)) V (h) ( l (l 1)) V (h) (1) If is a negative value, the uniform steady state is unstable for long wavelength modes, while the uniform flow is stable when is a positive value The neutral stability condition for the uniform steady state is given by s l (l 1) (11) 3V ( h) For small disturbances of long wavelength, the uniform traffic flow is stable if l (l 1) (1) 3V ( h) The neutral stability line in the parameter space ( x, a) (a 1 / ) is shown in Fig 1 for the extended model which is expressed by Eq() the word "CC" represents the coexisting curve and the word "SL" represents the spinodal line in the legend From Fig 1(a), it can be seen that the spinodal lines are lowered with taking into account larger value of with same n For n 3 and 5 with different, the curves related to the neutral stability lines and the coexisting curves are almost coincided in Fig 1(b) and (c) It demonstrates that considering three cars ahead for (ie n 3 ) is enough for the driver which is consistent with Ref [8] So considering two cars ahead for (ie n ) is enough for the driver from Fig 1 In fact, these number of cars are closely related to the selection of and, that is, the weighted coefficients of the and the relative velocity With different weight coefficients, a slightly different result will be obtained Fig 1 The phase diagram in the parameter space ( x, a) 1 Volume, Number, March 15

From Fig 1 it can be seen that the critical points and the neutral stability lines are lowered with taking into account more vehicles ahead and larger value of, which means the stability of the uniform traffic flow have been strengthened The traffic jams are thus suppressed efficiently Nonlinear Analysis The reductive perturbation method is applied to Eq () We introduce slow scales for space variable j as (see Ref [31]) and time variable t and define slow variables X and T for (13) X ( j bt ), T 3t, b is a constant to be determined We set the as (1) x j (t ) hc R( X, T ) Substituting Eqs (13) and (1) into Eq () and making the Taylor expansions about, we obtain the nonlinear partial differential equation given by Eq (15) 3 (b V ) X R 3 b V l l 1 b X R 7 V (l 1)3 l 3 (1 b )3 (b )3 1 3 T R X R3 b3 3 V l X R V 5 l 5 l 1 3 (l 1) l X R (3b ) X T R b V l 8 (15) 5 (l 1) l (1 b ) (b ) 1 (3b ) X T R b V l X R, 8 V '(hc ) d V ( x j ) d x j,v '''(hc ) x j hc d 3 V ( x j ) d x3j For simplicity, V ' and V ''' x j hc correspond to V '(hc ) and V '''(hc ) in the above equation and hereafter Near the critical point, (1 ) c and taking b V, the second- and third-order terms of are eliminated from Eq (15) Then Eq (15) can be rewritten as the simplified equation T R g1 3X R g X R3 [ g3 X R g X R3 g5 X R], (1) l (l 1), g1 V [9 l ((l 1)3 l 3 ) (7 9 ) 7 ], g V, g3 V, g V, 5 g5 1 V V [5 3 9 l ((l 1) l ) ( 9 18)] ( )[7 9 l ((l 1)3 l 3 ) 9 (3 )] 1 5 In order to derive the standard mkdv equation with higher order correction, we can do the same procedure as Ref [5] The propagation velocity A for the kink-antikink solution which is the solution of mkdv equation is as follows 11 Volume, Number, March 15

A 5 g g3 g g5 3g1 g (17) Substituting the values into Eq (17), we can obtain the value of propagation velocity for any vehicle that precede The solution of the mkdv equation is obtained R( X, T ) g1 A tanh g A ( X Ag1T ) (18) 5 Numerical simulation To check the validity of our theoretical results above, we carry out numerical simulation for the model described by Eq () and (5) under the periodic boundary condition First, we consider the impact of local small disturbances on the whole system The vehicles move with the constant h The initial conditions are chosen as follows x j () x j (1), x j () x j (1) 5, x j () x j (1) 5, ( j, 51) ( j ) ( j 51) the total number of cars is N 1, the safety distance is hc, vmax, a Fig shows the space-time evolution of the after a sufficiently long time for the extended model described by Eq () and (5) The space-time evolution of the for and are exhibited by the patterns (a), (b), (d) and (d) For and, those are exhibited by the patterns (c), (d), (d) and (d) The pattern (d) also exhibits the space-time evolution of the for and In patterns (a), (b) and (c), the traffic flow is unstable because the stability condition (1) is not satisfied The patterns (a) and (c) exhibit the coexisting phase in which the kink-antikink density waves appear as traffic jams and the density waves propagate backwards That is to say, when small disturbances are added to the uniform traffic flow, the disturbances are amplified with time and the uniform flow changes finally to inhomogeneous traffic flow The pattern (d) exhibits the freely moving phase after a sufficiently large time, that is, the traffic flow are stable with the same sensitivity The above analyses show that considering two cars that precede are enough for suppressing the traffic jams quickly and efficiently when or three cars when And one car is enough when The influence of cars ahead is almost invariant after when or when, which is consistent with the results in Fig 1 Fig 3 shows the profile obtained at sufficiently large time correspond to Fig From Fig 3, we find that the amplitude of the density wave decreases as the considered number of cars ahead and the value of increases In pattern (d) the density waves disappear and traffic flow is uniform over the whole space Summary An extended car-following model is proposed by introducing the of arbitrary number of cars and the relative velocity for suppressing the traffic jams based on Internet-connected vehicles The extended model has been analyzed by using the linear stability theory and the reductive perturbation method The stability condition of the extended model is obtained and the results show that the stability of traffic flow is improved by taking into account not only the of arbitrary number of cars ahead but also the relative velocity The kink-antikink soliton, solution of the mkdv equation near the critical point, is 1 Volume, Number, March 15

derived to describe the traffic jams From the numerical simulation, the kink-antikink soliton is found The simulation results confirm the linear stability analysis for the extended model and give the optimal state as n 3( ), n ( 1) and n 1( ), that is, considering both the of cars ahead and the 13 1 x 1 1 13 1 11 1 1 (a) car number 1 time time 1 1 1 13 1 x 1 1 13 1 11 1 1 (c) car number time x 1 11 (a) car number relative velocity are necessary to suppress the appearance of traffic jams efficiently based on Internet-connected vehicles From the theoretical analysis and the numerical simulation, we can conclude that the traffic jams are suppressed efficiently by taking into account not only the of arbitrary number of cars ahead but also the relative velocity The theoretical results are in good agreement with the simulation results x 1 11 time 1 1 (d) car number 55 55 5 5 5 5 5 5 5 5 3 5 35 35 3 3 (a) car number 1 5 55 35 55 Fig Space-time evolution of the after t=1, for small disturbances (b) car number 1 5 35 3 (c) car number 1 5 (d) car number 1 Fig 3 Headway profile of the density wave at t=198 correspond to Fig Acknowledgment This work is supported by National Natural Science Foundation of China (Grant No111155, 11115), Natural Science Foundation of Shaanxi Province (13J Q71), the new direction of the Young Scholar of Northwestern Polytechnical University and Foundation for Fundamental Research of Northwestern Polytechnical University (JC115) References [1] Schreckenberg, M, & Wolf, D E, editors (1998) Traffic and granular flow'97 Springer [] Helbing, D, Herrman, H J, Schreckenberg, M, & Wolf, D E, editors () Traffic and Granular 13 Volume, Number, March 15

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optimal velocity models Phys Rev E, 9, 1713-1-3 [8] Ge, H X, Dai, S Q, Dong L Y, & Xue, Y () Stabilization effect of traffic flow in an extended car-following model based on an intelligent transportation system application Phys Rev E, 7, 13-1- [9] Ge, H X, Dai, S Q, & Dong L Y () An extended car-following model based on intelligent transportation system application Phys A, 35, 53-58 [3] Li, Z P, & Liu, Y C () Analysis of stability and density waves of traffic flow model in an ITS environment Eur Phys J B, 53, 37-37 [31] Komatsu, T, & Sasa, S (1995) Kink soliton characterizing traffic congestion Phys Rev E, 5, 557-558 Lei Yu was born in 198 She received the BS degree from Henan Univ in, the MS degree and PhD degree from Northwestern Polytechnical Univ in and 1, respectively Her research interests are traffic flow modeling and simulation Bingchang Zhou was born in 1981 He received the MS degree and PhD degree from Northwestern Polytechnical Univ in 5 and 9, respectively His research interest is onlinear dynamics Zhongke Shi was born in 195 He received the BS degree from Northwestern Polytechnical Univ in 1981 and his MS degree and PhD degree from the same school His research interests are traffic control and control theory 15 Volume, Number, March 15