Glossary
- Autonomous Driving :
-
An autonomous driving vehicle is a self-driving vehicle that can move without a driver. Autonomous driving is realized through the use of an automated system in a vehicle: The automated system has control over the vehicle in traffic flow. For this reason, autonomous driving vehicle is often also called automated driving (or automatic driving) vehicle.
- Autonomous Driving in Framework of Three-Phase Traffic Theory:
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An autonomous driving in the framework of the three-phase traffic theory is the autonomous driving for which there is no fixed time headway to the preceding vehicle. This means the existence of an indifference zone in car-following for the autonomous driving vehicle.
- Bottleneck :
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Traffic breakdown occurs mostly at road bottlenecks. A road bottleneck can be a result of roadworks, on- and off-ramps, a decrease in the number of freeway lanes, road curves and road gradients, traffic signal, etc.
- Main Prediction of Three-Phase Traffic Theory:
- ...
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Acknowledgments
I would like to thank Sergey Klenov for the help and useful suggestions. We thank our partners for their support in the project “MEC-View – Object detection for autonomous driving based on Mobile Edge Computing,” funded by the German Federal Ministry of Economic Affairs and Energy.
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Appendices
Appendix A. Kerner–Klenov Microscopic Stochastic Traffic Flow Model
In this Appendix, we make explanations of the Kerner–Klenov stochastic microscopic three-phase model for human driving vehicles (Kerner and Klenov 2002, 2003, 2009) and model parameters used for simulations of mixed traffic flow presented in the main text.
Update Rules of Vehicle Motion
In a discrete model version of the Kerner-Klenov stochastic microscopic three-phase model used in all simulations presented in the main text, rather than the continuum space coordinate (Kerner and Klenov 2002), a discretized space coordinate with a small enough value of the discretization space interval δx is used (Kerner and Klenov 2009). Consequently, the vehicle speed and acceleration (deceleration) discretization intervals are δv = δx/τ and δa = δv/τ, respectively, where τ is time step. Because in the discrete model version discrete (and dimensionless) values of space coordinate, speed, and acceleration are used, which are measured, respectively, in values δx, δv, and δa, and time is measured in values of τ, value τ in all formulas is assumed below to be the dimensionless value τ = 1. In the discrete model version used for all simulations, the discretization cell δx = 0.01 m is used.
A choice of δx = 0.01 m made in the model determines the accuracy of vehicle speed calculations in comparison with the initial continuum in space stochastic model of (Kerner and Klenov 2002). We have found that the discrete model exhibits similar characteristics of phase transitions and resulting congested patterns at highway bottlenecks as those in the continuum model at δx that satisfies the conditions
where model parameters for driver deceleration and acceleration b, a, a(a), a(b), and a(0) will be explained below.
Update rules of vehicle motion in the discrete model for identical drivers and identical vehicles moving in a road lane are as follows (Kerner and Klenov 2009):
where the index n corresponds to the discrete time tn = τn, n = 0, 1, ..., vn is the vehicle speed at time step n, a is the maximum acceleration, and \( {\tilde{v}}_n \) is the vehicle speed without speed fluctuations ξn:
The subscript ℓ marks variables related to the preceding vehicle, vs,n is a safe speed at time step n, vfree is the free flow speed in free flow, ξn describes speed fluctuations, gn is a space gap between two vehicles following each other, and Gn is the synchronization space gap; all vehicles have the same length d. The vehicle length d includes the mean space gap between vehicles that are in a standstill within a wide moving jam. Values an ≥ 0 and bn ≥ 0 in (19) and (20) restrict changes in speed per time step when the vehicle accelerates or adjusts the speed to that of the preceding vehicle.
Synchronization Space Gap and Hypothetical Steady States of Synchronized Flow
Equations (19) and (20) describe the adaptation of the vehicle speed to the speed of the preceding vehicle, i.e., the speed adaptation effect in synchronized flow. This vehicle speed adaptation takes place within the synchronization gap Gn: At
the vehicle tends to adjust its speed to the speed of the preceding vehicle. This means that the vehicle decelerates if vn > vℓ,n and accelerates if vn < vℓ,n.
In (19), the synchronization gap Gn depends on the vehicle speed vn and on the speed of the preceding vehicle vℓ,n:
where k > 1 is constant; ⌊z⌋ denotes the integer part of z.
The speed adaptation effect within the synchronization distance is related to the hypothesis of the three-phase theory: Hypothetical steady states of synchronized flow cover a 2D region in the flow–density (Fig. 18a). Boundaries F, L, and U of this 2D region shown in Fig. 18a are, respectively, associated with the free flow speed in free flow, a synchronization space gap G, and a safe space gap gsafe. A speed function of the safe space gap gsafe(v) is found from the equation
Steady speed states for the Kerner–Klenov traffic flow model in the flow–density (a, b) and in the space gap–speed planes (c). In (a, b), L and U are, respectively, lower and upper boundaries of 2D regions of steady states of synchronized flow. In (b), J is the line J whose slope is equal to the characteristic mean velocity vg of the downstream front of a wide moving jam; in the flow–density plane, the line J represents the propagation of the downstream front of the wide moving jam with time-independent velocity vg. F, free flow; S, synchronized flow
Respectively, as for the continuum model (see Sec. 16.3 of the book (Kerner 2004)), for the discrete model hypothetical steady states of synchronized flow cover a 2D region in the flow–density plane (Fig. 18a, b). However, because the speed v and space gap g are integers in the discrete model, the steady states do not form a continuum in the flow–density plane as they do in the continuum model. The inequalities
define a 2D region in the space gap–speed plane (Fig. 18c) in which the hypothetical steady states exist for the discrete model, when all model fluctuations are neglected.
In (26), we have taken into account that in the hypothetical steady states of synchronized flow vehicle speeds and space gaps are assumed to be time-independent, and the speed of each of the vehicles is equal to the speed of the associated preceding vehicle: v = vℓ. However, due to model fluctuations, steady states of synchronized flow are destroyed, i.e., they do not exist in simulations; this explains the term “hypothetical” steady states of synchronized flow. Therefore, rather than steady states, some nonhomogeneous in space and time traffic states occur. In other words, steady states are related to a hypothetical model fluctuation-less limit of homogeneous in space and time vehicle motion that does not realized in real simulations. Driver time delays are described through model fluctuations. Therefore, any application of the Kerner–Klenov stochastic microscopic three-phase traffic flow model without model fluctuations has no sense. In other words, for the description of real spatiotemporal traffic flow phenomena, model speed fluctuations incorporated in this model are needed.
Model Speed Fluctuations
In the model, random vehicle deceleration and acceleration are applied depending on whether the vehicle decelerates or accelerates or else maintains its speed:
State of vehicle motion Sn + 1 in (27) is determined by formula
In (27), ξb, ξ(0), and ξa are random sources for deceleration and acceleration that are as follows:
pb is the probability of random vehicle deceleration, pa is the probability of random vehicle acceleration, p(0) and a(0) ≤ a are constants, r = rand(0, 1), Θ(z) = 0 at z < 0 and Θ(z) = 1 at z ≥ 0, and a(a) and a(b) are model parameters (see Table 1), which in some applications can be chosen as speed functions a(a) = a(a)(vn) and a(b) = a(b)(vn).
Stochastic Time Delays of Acceleration and Deceleration
To simulate time delays either in vehicle acceleration or in vehicle deceleration, an and bn in (20) are taken as the following stochastic functions
r1 = rand(0, 1), p1 is constant, and p0 = p0(vn) and p2 = p2(vn) are speed functions (see Table 1).
Simulations of Slow-To-Start Rule
In the model, simulations of the well-known effect of the driver time delay in acceleration at the downstream front of synchronized flow or a wide moving jam known as a slow-to- start rule (Barlović et al. 1998; Takayasu and Takayasu 1993) are made as a collective effect through the use of Eqs. (19) and (20) and a random value of vehicle acceleration (32). Eq. (32) with P0 = p0 < 1 is applied only if the vehicle did not accelerate at the former time step (Sn ≠ 1); in the latter case, a vehicle accelerates with some probability p0 that depends on the speed vn; otherwise P0 = 1 (see formula (34)).
The mean time delay in vehicle acceleration is equal to
From formula (36), it follows that the mean time delay in vehicle acceleration from a standstill within a wide moving jam (i.e., when in formula (36) the speed vn = 0) is equal to
The mean time delay in vehicle acceleration from a standstill within a wide moving jam determines the parameters of the line J in the flow–density plane (Fig. 18b).
Probability p0(vn) in (34) is chosen to be an increasing speed function (see Table 1). Because the speed within synchronized flow is larger than zero, the mean time delay in vehicle acceleration at the downstream front of synchronized flow that we denote by
is shorter than the mean time delay in vehicle acceleration at the downstream front of the wide moving jam \( {\tau}_{\mathrm{de}1}^{\left(\mathrm{acc}\right)}(0) \):
Safe Speed
In the model, the safe speed vs,n in (16) is chosen in the form
\( {v}_{\mathrm{\ell}}^{\left(\mathrm{a}\right)} \) is an “anticipation” speed of the preceding vehicle that will be considered below, and the function
in (40) is related to the safe speed v(safe)(gn, vℓ,n) in the model by Krauß et al. (1997), which is a solution of the Gipps’s Eq. [32]
where Xd(u) is the braking distance that should be passed by the vehicle moving first with the speed u before the vehicle can come to a stop.
The condition (42) enables us to find the safe speed v(safe) as a function of the space gap gn and speed vℓ,n provided Xd(u) is a known function. In the case when the vehicle brakes with a constant deceleration b, the change in the vehicle speed for each time step is −bτ except the last time step before the vehicle comes to a stop. At the last time step, the vehicle decreases its speed at the value bτβ, where β is a fractional part of u/bτ. According to formula (17) for the displacement of the vehicle for one time step, the braking distance Xd(u) is (Krauß et al. 1997)
From (43), it follows (Krauß et al. 1997)
α = ⌊u/bτ⌋ is an integer part of u/bτ.
The safe speed v(safe) as a solution of Eq. (42) at the distance Xd(u) given by (44) has been found by Krauß et al. (1997)
where
The safe speed in the model by Krauß et al. (1997) provides collisionless motion of vehicles if the time gap gn/vn between two vehicles is greater than or equal to the time step τ, i.e., if gn ≥ vnτ (Krauß 1998). In the model, it is assumed that in some cases, mainly due to lane changing or merging of vehicles onto the main road within the merging region of bottlenecks, the space gap gn can become less than vnτ. In these critical situations, the collision-less motion of vehicles in the model is a result of the second term in (40) in which some prediction \( \left({v}_{\mathrm{\ell}}^{\left(\mathrm{a}\right)}\right) \) of the speed of the preceding vehicle at the next time step is used. The related “anticipation” speed \( {v}_{\mathrm{\ell}}^{\left(\mathrm{a}\right)} \) at the next time step is given by formula
where \( {v}_{\mathrm{\ell},n}^{\left(\mathrm{safe}\right)} \) is the safe speed (41) and (45, 46, 47) for the preceding vehicle and gℓ,n is the space gap in front of the preceding vehicle. Simulations have shown that formulas (40), (41), and (45), (46), (47), (48) lead to collision-less vehicle motion over a wide range of parameters of the merging region of on-ramp bottlenecks (Appendix D).
In hypothetical steady states of traffic flow (Fig. 18a), the safe space gap gsafe is determined from condition v = vs. In accordance with Eqs. (40, 41, and 42), at a given v in steady traffic states v = vℓ for the safe speed vs, we get
and, therefore,
Thus, for hypothetical steady states of traffic flow τsafe = τ = 1 s.
In (Kerner 2017a) it has been shown that the Kerner–Klenov model (16)–(21), (23), (24), (27)–(35), (40), (41), and (45)–(48) is a Markov chain: At time step n + 1, values of model variables vn + 1, xn + 1, and Sn + 1 are calculated based only on their values vn, xn, and Sn at step n.
It should be noted that after the Kerner–Klenov car-following model with indifference zones in car-following based on the three-phase theory (dashed region in Fig. 3) has been introduced (Kerner and Klenov 2002) (as mentioned, in this review entry a discrete version of this model of Ref. (Kerner and Klenov 2009) has been used), a number of different traffic flow models incorporating some of the hypotheses of the three-phase theory have been developed, and many results with the use of these models have been found (e.g., (Borsche et al. 2012; Davis 2004a, b, 2006a, b, 2007, 2008, 2014; Gao et al. 2007, 2009; Hausken and Rehborn 2015; He et al. 2010; Hoogendoorn et al. 2008; Jiang and Wu 2004, 2005, 2007a; Jiang et al. 2007, 2014, 2015, 2017; Jiang and Wu 2007b; Jin et al. 2010, 2015; Jin and Wang 2011; Kerner 2015b; Kerner and Klenov 2003, 2006, 2018; Kerner et al. 2002, 2006a, b, 2007, 2011, 2013a, b, 2014; Klenov 2010; Gasnikov et al. 2013; Kokubo et al. 2011; Knorr and Schreckenberg 2013; Lee et al. 2004; Lee and Kim 2011; Li et al. 2007; Neto et al. 2011; Qian et al. 2017; Pottmeier et al. 2007; Rehborn and Klenov 2009; Rehborn et al. 2011a, b, 2017; Rehborn and Koller 2014; Siebel and Mauser 2006; Tian et al. 2009, 2012, 2016a, b, c; Wang et al. 2007; Wu et al. 2008; Xiang et al. 2013; Yang et al. 2013, 2018; Zhang et al. 2012)). A review of some of these models that are related to the class of cellular automation models that can be found in this Encyclopedia has been recently done by Tian et al. (2018).
Appendix B. Discrete Version of Classical ACC Model
In simulations of the classical ACC model (4), as in the model of human driving vehicles (Appendix A), we use the discrete time t = nτ, where n = 0, 1, 2, ..; τ = 1 s is time step. Therefore, the space gap to the preceding vehicle is equal to gn = xℓ,n − xn − d, and the relative speed is given by Δvn = vℓ,n − vn, where xn and vn are coordinate and speed of the ACC-vehicle, xℓ,n and vℓ,n are coordinate and speed of the preceding vehicle, and d is the vehicle length that is assumed the same one for autonomous driving and human driving vehicles. Correspondingly, the classical model of the dynamics of ACC-vehicle (4) can be rewritten as follows (Kerner 2016, 2017a):
The ACC-vehicles move in accordance with Eq. (51) where, in addition, the following formulas are used:
⌊z⌋ denotes the integer part of z. Through the use of formula (52), acceleration and deceleration of the ACC-vehicles are limited by some maximum acceleration amax and maximum deceleration bmax, respectively. Owing to the formula (53), the speed of the ACC-vehicle vn + 1 at time step n + 1 is limited by the maximum speed in free flow vfree and by the safe speed vs,n to avoid collisions between vehicles. The maximum speed in free flow vfree and the safe speed vs,n are chosen, respectively, the same as those in the microscopic model of human driving vehicles (Appendix A). It should be noted that the model of ACC-vehicle merging from the on-ramp onto the main road is similar to that for human driving vehicles (see Appendix D).
Simulations show that the use of the safe speed in formula (53) does not influence on the dynamics of the ACC-vehicles (4) in free flow outside the bottleneck. However, due to vehicle merging from the on-ramp onto the main road, time headway of the vehicle to the preceding vehicle can be considerably smaller than \( {\tau}_{\mathrm{d}}^{\left(\mathrm{ACC}\right)} \). Therefore, formula (53) allows us to avoid collisions of the ACC-vehicle with the preceding vehicle in such dangerous situations. Moreover, very small values of time headway can occur in congested traffic; formula (53) prevents vehicle collisions in these cases also.
Appendix C. Discrete Version of TPACC Model
The model for human driving vehicles (Kerner and Klenov 2002, 2003, 2009) used in all simulations is discrete in time (Appendix A). Therefore, we simulate TPACC model (9) with discrete time tn = τn, n = 0, 1, .... Respectively, TPACC model (9) should be rewritten as follows:
where Gn = vnτG and τp < τG.
Safety Conditions
When gn < gsafe,n, the TPACC-vehicle should move in accordance with some safety conditions to avoid collisions between vehicles (Fig. 3). A collision-free TPACC-vehicle motion is described as made in (Kerner 2016) for the classical model of ACC:
where the TPACC acceleration and deceleration are limited by amax and bmax, respectively; the speed vn + 1 (56) at time step n + 1 is limited by the maximum speed vfree and by the safe speed vs,n that have been chosen, respectively, the same as those in the model of human driving vehicles; ⌊z⌋ denotes the integer part of z.
“Indifference Zone” in Car-Following
In accordance with Eq. (56), condition \( {v}_{\mathrm{c},n}^{\left(\mathrm{TPACC}\right)}\le {v}_{\mathrm{s},n} \) is equivalent to condition gn ≥ gsafe,n. Under this condition, from the TPACC model (54)–(56), it follows that when time headway \( {\tau}_n^{\left(\mathrm{net}\right)}={g}_n/{v}_n \) of the TPACC-vehicle to the preceding vehicle is within the range
the acceleration (deceleration) of the TPACC-vehicle does not depend on time headway. In (57), τsafe,n = gsafe,n/vn is a safe time headway, and it is assumed that vn > 0.
In accordance with (57), for the TPACC model (54)–(56), there is no fixed desired time headway to the preceding vehicle (Fig. 3). This means that in the TPACC model (54)–(56), there is “indifference zone” in the choice of time headway in car-following. This is in contrast with the classical ACC model (4) for which there is a fixed desired time headway in car-following. It should be noted that formula (57) for the indifference zone in time headway of TPACC is a discrete version of formula (13) for the indifference zone in time headway of TPACC discussed in the main text.
Operating Points
From formula for the safe speed vs,n in (56) that is given in Appendix A, we find that the safe time headway τsafe,n in (13) for the operating points of TPACC model (54), (55), and (56) is a constant value that is equal to τsafe = τ = 1 s. In operating points of TPACC model (54), (55), and (56), a(TPACC) = 0; respectively, v = vℓ, gsafe(v) ≤ g ≤ G(v), and v = vfree at g > G(v), where gsafe(v) = vτ and G(v) = vτG. The operating points of the TPACC model (54), (55), and (56) cover a 2D region in the space gap–speed plane (dashed 2D region in Fig. 19a). The inequalities v ≤ vfree, g ≤ G(v), and g ≥ gsafe(v) define a 2D region in the space gap–speed plane (Fig. 19a) in which the operating points exist for the discrete version of TPACC model (54), (55), and (56).
Operating points of TPACC model (54)–(56) presented in the space gap–speed (a), flow–density (b), speed–flow (c), and speed–density (c) planes. Model parameters τG = 1.4 s, τp = 1.3 s, τsafe = 1 s, vfree = 30 m/s (108 km/h), vehicle length (including the mean space gap between vehicles stopped within a wide moving jam) d = 7.5 m
It should be noted that the speed v and space gap g are integer in the discrete version of TPACC model (54), (55), and (56). Therefore, the operating points do not form a continuum in the space gap–speed plane as they do in the continuum version of TPACC model (9).
From Fig. 19, we can see that under conditions gsafe(v) ≤ g ≤ G(v) for each given speed v > 0 of TPACC, there is no fixed time headway to the preceding vehicle in operating points of the TPACC model (dashed 2D regions in Fig. 19), as explained in section “ACC in Framework of Three-Phase Theory (TPACC)” (Fig. 3).
The discretization interval of TPACC acceleration (deceleration) made in TPACC model (54), (55), and (56) is chosen to be an extremely small value that is equal to δa = 0.01 m/s2 (see Appendix A). Therefore, the maximum value of a small round down of \( {a}_n^{\left(\mathrm{TPACC}\right)} \) in Eqs. (55) and (56) through the application of the floor operator \( \left\lfloor {a}_n^{\left(\mathrm{TPACC}\right)}\right\rfloor \) is less than 0.01 m/s2, and it is, therefore, negligible. We have tested that no conclusions about physical features of TPACC dynamic behavior have been changed, when the continuum in space model of human driving vehicles of Ref. (Kerner and Klenov 2003) and, respectively, the continuum in space TPACC model version (without the floor operator) are used (the reason for the use of the discrete in space model for human driving vehicles of Ref. (Kerner and Klenov 2009) that leads to Eqs. (55) and (56) has been explained in (Kerner and Klenov 2009) as well as in Appendix A of the book (Kerner 2017a)).
Simulations show that the use of the safe speed in formula (56) does not influence on the dynamics of the TPACC-vehicles (54) in free flow outside the bottleneck. However, formula (56) allows us to avoid collisions of the TPACC-vehicle with the preceding vehicle in dangerous situations that can occur at the bottleneck as well as in congested traffic.
Appendix D. Model of On-Ramp Bottleneck
An on-ramp bottleneck consists of two parts (Fig. 20):
-
(i)
The merging region of length Lm where vehicle can merge onto the main road from the on-ramp lane.
-
(ii)
A part of the on-ramp lane of length Lr upstream of the merging region where vehicles move in accordance with the model of Appendix A. The maximal speed of vehicles is vfree = vfree on.
At the beginning of the on-ramp lane (\( x={x}_{\mathrm{on}}^{\left(\mathrm{b}\right)} \)), the flow rate to the on-ramp qon is given through boundary conditions that are the same as those that determine the flow rate qin at the beginning of the main road (see Appendix E below).
Model of Vehicle Merging at Bottleneck
Vehicle Speed Adaptation Within Merging Region of Bottleneck
For the on-ramp bottleneck, when a vehicle is within the merging region of the bottleneck, the vehicle takes into account the space gaps to the preceding vehicles and their speeds both in the current and target lanes. Respectively, instead of formula (19), in (18) for the speed vc,n, the following formula is used:
\( \Delta {v}_r^{(2)} \) is constant (see Table 2).
Superscripts + and − in variables, parameters, and functions denote the preceding vehicle and the trailing vehicle in the “target” (neighboring) lane, respectively. The target lane is the lane into which the vehicle wants to change.
The safe speed vs,n in (16) and (18) for the vehicle that is the closest one to the end of the merging region is chosen in the form
(see Table 2).
Safety Conditions for Vehicle Merging
Vehicle merging at the bottleneck occurs, when safety conditions (∗) or safety conditions (∗∗) are satisfied.
Safety conditions (∗) are as follows:
\( {\Delta v}_r^{(1)}>0 \) is constant (see Fig. 20 and Table 2).
Safety conditions (∗∗) are as follows:
where
λb is constant. In addition to conditions (64), the safety condition (∗∗) includes the condition that the vehicle should pass the midpoint
between two neighboring vehicles in the target lane, i.e., conditions
should also be satisfied.
Speed and Coordinate of Vehicle after Vehicle Merging
The vehicle speed after vehicle merging is equal to
Under conditions (∗), the vehicle coordinates xn remains the same. Under conditions (∗∗), the vehicle coordinates xn is equal to
Merging of ACC-Vehicle or TPACC-Vehicle at On-Ramp Bottleneck
Here we consider rules of the merging of an ACC-vehicle at the on-ramp bottleneck presented in (Kerner 2017a) and used in simulations. The same rules have also been used in simulations of the merging of a TPACC-vehicle from the on-ramp lane onto the main road at the bottleneck.
In the on-ramp lane, an ACC-vehicle or a TPACC-vehicle moves in accordance with the ACC model (51), (52), and (53) or in accordance with the TPACC model (54), (55), and (56), respectively. The maximal speed of the ACC-vehicle or the TPACC-vehicle in the on-ramp lane is vfree = vfree on. The safe speed vs,n in (53) for the ACC-vehicle and in (56) for the TPACC-vehicle that is the closest one to the end of the merging region is the same as that for human driving vehicles that is given by formula (61).
An ACC-vehicle or a TPACC-vehicle merges from the on-ramp lane onto the main road, when some safety conditions (∗) or safety conditions (∗∗) are satisfied for the ACC- vehicle or the TPACC-vehicle. Safety conditions (∗) for ACC-vehicles and TPACC-vehicles are as follows:
where \( {\widehat{v}}_n \) is given by formula (63). Safety conditions (∗∗) are given by formulas (64), (65), (66), and (67), i.e., they are the same as those for human driving vehicles. Respectively, as for human driving vehicles, the ACC-vehicle speed and its coordinate or the TPACC-vehicle speed and its coordinate after the ACC-vehicle or the TPACC-vehicle has merged from the on-ramp onto the main road are determined by formulas (68) and (69).
A question can arise from the choice of the model time step τ = 1 s in Eqs. (54), (55), and (56) that have been used for numerical simulations of TPACC model (9): In TPACC model (54), (55), and (56), the time step τ = 1 s determines the safe space gap gsafe = vτ under hypothetical steady-state conditions in which all vehicles move at time-independent speed v. Contrarily to the TPACC model (54), (55), and (56), typical ACC controllers in vehicles that on the market have update time intervals τ of 100 ms or less. Indeed, there may be some very dangerous traffic situations in real traffic in which the safe time headway for an ACC-vehicle is quickly reached, and, therefore, the ACC-vehicle must decelerate strongly already after a time interval that is a much shorter than 1 s to avoid the collision with the preceding vehicle. Therefore, to avoid collisions, real ACC controllers must have update time intervals τ of 100 ms or less. However, at model time step τ = 1 s through the choice in the mathematical formulation of the safe speed in TPACC model (54), (55), and (56) and in the model of human driving vehicles, collision-less traffic flow is guaranteed in any dangerous traffic situation that can occur in simulations of traffic flow.
In other words, to disclose the physics of TPACC it is sufficient the choice of the update time τ = 1 s in simulations of TPACC behavior. To explain this, we should note that Eqs. (55) and (56) effect on TPACC dynamics only under condition gn < gsafe,n, i.e., when the space gap becomes smaller than the safe one. This is because the physics of TPACC disclosed in this entry is solely determined by Eq. (54): Under condition gn ≥ gsafe,n, Eqs. (55) and (56) do not change TPACC acceleration (deceleration) calculated through Eq. (54).
Appendix E. Boundary Conditions for Mixed Traffic Flow
Open boundary conditions are applied. At the beginning of the road, new vehicles are generated one after another in each of the lanes of the road at time instants
In (71), τin = 1/qin, qin is the flow rate in the incoming boundary flow per lane, and ⌈z⌉ denotes the nearest integer greater than or equal to z. Human driving and autonomous driving vehicles are randomly generated at the beginning of the road with the rates related to chosen values of the flow rate qin and the percentage γ of the autonomous driving vehicles in mixed traffic flow: (i) At time instant t(m) (71), a new vehicle is human driving vehicle, when condition
is satisfied where r2 = rand(0, 1). (ii) At time instant t(m) (71), a new vehicle is autonomous driving vehicle, when the opposite condition
is satisfied. The same procedure of random generation of human driving and autonomous driving vehicles is applied at the beginning of the on-ramp lane. In this case, however, in (71) the flow rate qin should be replaced by the on-ramp inflow rate qon.
A new vehicle appears on the road only if the distance from the beginning of the road (x = xb) to the position x = xℓ,n of the farthest upstream vehicle on the road is not smaller than the safe distance vℓ,nτ + d:
where n = t(m)/τ. Otherwise, condition (74) is checked at time (n + 1)τ that is the next one to time t(m) (71) and so on, until the condition (74) is satisfied. Then the next vehicle appears on the road. After this occurs, the number m in (71) is increased by 1.
The speed vn and coordinate xn of the new vehicle are
The flow rate qin is chosen to have the value vfreeτin integer. In the initial state (n = 0), all vehicles have the free flow speed vn = vfree, and they are positioned at space intervals xℓ,n − xn = vfreeτin.
After a vehicle has reached the end of the road, it is removed. Before this occurs, the farthest downstream vehicle maintains its speed and lane. For the vehicle following the farthest downstream one, the “anticipation” speed \( {v}_{\mathrm{\ell}}^{\left(\mathrm{a}\right)} \) in (40) is equal to the speed of the farthest downstream vehicle.
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Kerner, B.S. (2019). Autonomous Driving in the Framework of Three-Phase Traffic Theory. In: Kerner, B. (eds) Complex Dynamics of Traffic Management. Encyclopedia of Complexity and Systems Science Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8763-4_724
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