*Models and Methods for Intelligent Highway Routing of Human-Driven and Connected… DOI: http://dx.doi.org/10.5772/intechopen.94332*

system measures of the network would be maximized, while the drivers would choose the path provided by the RG control. They discussed their mathematical formulation in detail and provided the solution finding algorithm.

In 1999 and then with some modifications in 2002, Apostolos Kotsialos et al. in [28, 29], considered the design of an integrated traffic control system for motorway networks with the use of ramp metering, motorway-to-motorway control, and route guidance. They offered a generic problem formulation in the format of a discrete time optimal control problem. They assumed that both RM control measures and RG are available. The METANET model was used for the description of traffic flow. A hypothetical test network was considered to evaluate the performance of the proposed control system. The control measure considered was the minimisation of the travel time spent (TTS). The results showed the high efficiency of the proposed control system.

In 2004, Karimi et al. [30] considered the integration of dynamic RG and RM based on MPC. They used the dynamic route guidance panels (DRIPs) as both a control tool and an information provider to the drivers, and ramp metering as a control tool to spread the congestion over the network. This resulted in a control strategy that reduced the total time spent by optimally re-routing traffic over the available alternative routes in the network, and also kept the difference between the travel times shown on the DRIPs and the travel times actually realized by the drivers as small as possible. The simulations done for the case study showed that rerouting of traffic and on-ramp metering using MPC has lead to a significant improvement in performance.

In 2015, Yu Han et al. [31] proposed an extended version of the CTM first proposed in [2] with the ability to reproduce the capacity drop at both the on-ramp bottleneck and the lane drop bottleneck. Based on this model, a linear quadratic model predictive control strategy for the integration of dynamic RG and RM was offered with the objective of minimizing the TTS of a traffic network. In this paper, a RG model based on the perceived travel time of each route by drivers was also offered. If the instantaneous travel time of each route is provided to travelers, the perceived travel time is assumed to be the same as the instantaneous travel time. If not, the perceived travel time is assumed to be the free flow travel time. The splitting rates at a bifurcation are determined by the well-known Logit model [26, 30]. A test case network containing both on-ramp bottlenecks and lane drop bottlenecks was used to investigate the effectiveness of the proposed framework and the results showed the improvement the proposed control strategy brought for the network performance.

In 2017, Cecilia Pasquale et al. [22] offered a multi-class control scheme for freeway traffic networks with the integration of RM and RG in order to reduce the TTS and the total emissions in a balanced way. Their two controllers were feedback predictive controllers and it was shown how this choice for their controllers can benefit the performance of the controllers. They applied the multi-class METANET model and the multi-class macroscopic VERSIT+ model for prediction of the traffic dynamics in the network. In addition, they designed a controller gain selector to compute the gains of the RM and RG controllers. The simulation results showed significant improvements of the freeway network performance, in terms of reduction of the TTS and the total emissions.

In 2018, Hirsh Majid et al. [32] designed integrated traffic control strategies for highway networks with the use of RG and RM. The highway network was simulated using the LWR model. A control algorithm was designed to solve the proposed problem, based on the inverse control technique and variable structure control (super twisting sliding mode). Three case studies were tested in the presence of an on-ramp at each alternate route and where there was a capacity constraint in the

where *β<sup>N</sup>*

*β*C *m*,*n*,*j*

techniques.

results in:

route.

**128**

*m*,*n*,*j*

ð Þ¼ *<sup>k</sup> <sup>β</sup>*<sup>C</sup>

*m*,*n*,*j*

*β<sup>m</sup>*,*n*,*<sup>j</sup>*

**2.4 Integration of ramp metering and route guidance**

combination of these two main traffic management techniques.

ð Þ*k* is the nominal splitting rate, *KP* is a gain, Δ*τn*,*j*ð Þ*k* is the instanta-

ð Þþ *<sup>k</sup>* � <sup>1</sup> *KP* <sup>Δ</sup>*τn*,*j*ð Þ� *<sup>k</sup>* <sup>Δ</sup>*τn*,*j*ð Þ *<sup>k</sup>* � <sup>1</sup> <sup>þ</sup> *KI*Δ*τn*,*j*ð Þ*<sup>k</sup>* (30)

neous travel time difference between the secondary and primary direction from *n* to

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

Another possible class of RG strategies is *iterative strategies*, where the splitting rate is computed by iteratively running different simulations in real time with different RG, in order to achieve conditions of either user equilibrium or system optimum [24, 25]. Iterative strategies are very beneficial, however, their high computational effort is a major drawback for this category of RG control

It is also interesting to describe how drivers react to travel time information and

how they adapt their route choice. A well-known behavior model used for this purpose is the logit model [26], which is used to model all kinds of consumer behavior based on the cost of several alternatives. The lower the cost of an alternative, the more consumers will choose that alternative. In the case of traffic management, consumers are the drivers, and the cost is the comfort, safety, or travel time of the alternative routes to reach the desired destination. The logit model calculates the probability that a driver chooses one of more alternatives based on the difference in travel time between the alternatives. Assume that we have two possible choices *m*<sup>1</sup> and *m*<sup>2</sup> at node *n* to get to destination *j*. For the calculation of the split rates out of the travel time difference between two alternatives, the logit model

ð Þ¼ *<sup>k</sup>* exp *σθ<sup>n</sup>*,*m*,*<sup>j</sup>*ð Þ*<sup>k</sup>*

for *m* ¼ *m*<sup>1</sup> or *m* ¼ *m*2, where *θ<sup>n</sup>*,*m*1,*<sup>j</sup>*ð Þ*k* is the travel time shown on the DRIP at node *n* to travel to destination *j* via link *m*. The parameter *σ* describes how drivers react on a travel time difference between two alternatives. The higher *σ*, the less travel time difference is needed to convince drivers to choose the fastest alternative

The RM and RG controllers are both feedback and predictive controllers as they apply not only on the real-time measurements of the system to calculate the control actions, but they also use information about the prediction of the system evolution. The combination of RM and RG controllers has shown promising results in network performance from the point of view of different performance measures. The focus of this section is on providing a review on the related studies on the

In 1990, a study performed by Iida et al. [27] considered the development of an improved on-ramp traffic control technique of urban expressway. They extended the conventional LP control method to consider the multiple paths between onramps and off-ramps of the test case network and also the route choice behavior of drivers. They assumed that in the future, the drivers would have the travel information offered by the route guidance system. Their formulation combined the user equilibrium with the available LP traffic control formulation at the time. In their problem statement, the goal was to determine the optimal metering rate so that the

exp *σθ<sup>n</sup>*,*m*1,*<sup>j</sup>*ð Þ*<sup>k</sup>* <sup>þ</sup> exp *σθ<sup>n</sup>*,*m*2,*<sup>j</sup>*ð Þ*<sup>k</sup>* (31)

*j*. In proportional-integral regulators, the splitting rate is

where *KP* and *KI* are other controller gains.

network. The objective was to avoid congestion on the main road and to balance the traffic flow on the alternate routes. The obtained results showed that the proposed algorithms could establish user equilibrium between two alternate routes even when the on-ramps have different traffic demands.

**3. Merging behavior at on-ramps and off-ramps for CAVs**

*Models and Methods for Intelligent Highway Routing of Human-Driven and Connected…*

self-organize in such a way that freeway traffic flow is optimized.

combat environmental issues caused by excessive fuel consumption.

merge junctions means that overall throughput of the highway is increased, individual waiting time is reduced and the wastage of fuel (energy) in idling vehicles stuck in traffic is also reduced. Therefore, it is evident that improvements to intelligent highways will have an impact on the economy as well as helping

the entire freeway can become blocked.

*DOI: http://dx.doi.org/10.5772/intechopen.94332*

**3.1 Standard problem formulation**

*On-ramp merging regions and infrastructure model.*

**Figure 2.**

**131**

In transportation networks, overall highway system efficiency can be severely reduced due to delays caused at on-ramps and off-ramps. If the merge process is incorrectly handled, merging lanes can have an overflow effect which causes the entire highway to become congested. This effect is caused by slower moving vehicles facing congestion in outer lanes near the merge junction deciding to switch into inner lanes in order to move faster. Therefore, even vehicles in the inner high speed lanes have to slow down. Overtime with continuing merge lane congestion,

In fact, in human-driven vehicles, this issue is further compounded due to the lack of cooperation and limited visibility for decision making. However, the introduction of CAVs has led to a lot more information becoming available for improving this overall merging process. In addition to the improved local sensing on-board modern CAVs such as 360<sup>∘</sup> radar and vision based sensors, most of the increased information comes from improvements in Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication methods. These communication protocols enable individual vehicles to broadcast their intent and status, receive command velocities to enable optimal flow and also collaborate with each other to

Effectively managing the highway merge problem has multiple benefits, both to the end users and the entire transportation system as well. Reduced time in traffic at

Most approaches to solving the highway merging problem use a similar structure to model the physical highway on-ramp. **Figure 2** shows an abstracted model of a highway on-ramp. A control zone is defined, where all vehicles in this zone communicate with a central controller and each other to decide individual optimum

In 2019, Martin Gregurić et al. [33] proposed the approach of coordination between controlling on-ramp flows with ramp metering (RM) and dynamic route guidance information systems (DRGIS), which reroute vehicles from congested parts of the motorway. DRGIS is used to inform drivers about current or expected travel times and queue lengths so that they may reconsider their choice for a certain route. It can be seen that DRGIS can directly impact on traffic demand at the urban traffic system by informing the drivers about travel times on its crucial segments. Reduced traffic demand on congested urban motorway section or at congested on-ramp in coordination with the adequate ramp metering control strategies can prevent "spill-back effect" and increase overall throughput of the urban motorways.

#### **2.5 Summary**

To conclude, in this section, a review on the integration of RM and RG controllers was presented. The section started by describing the two most commonly used discrete first-order and second-order traffic flow models. Then, it continued with an overview of the popular RM and RG control strategies and it finished by discussing most of the important studies on the integration of RM and RG. Most of them considered TTS as the performance metric and applied an MPC optimization framework since it reduces the computation efforts required to solve the optimization problem specially if the traffic evolution model used was the METANET model since it makes the formulation non-linear and non-convex. Overall, all the studies reviewed here have shown improvements in the network performance in comparison with the case of having either of these controllers alone.

The discussion so far has centered around the macro-level control of intelligent highways. However, an integral component of the transportation system of a smart city is the micro-level control of autonomous vehicles. It is, therefore, imperative that we cover some micro-level details pertaining to the CAVs. The following sections address problems related to the micro-level vehicle coordination. Commonly found interactions include highway merging, off-ramp exit, vehicle overtaking and lane changing. Focus is placed on on-ramp merging and overtaking in presence of incoming traffic, as these two tasks combined encompass most of the complexities involved in inter-vehicle interactions.

The selection of the on-ramp merging task (see Section 3) as a key area to be explored is due to both the extent of variables involved in coordinating this process and the dynamic nature of the process itself. In fact this is one of the tasks that today's autonomous vehicles find difficult to carry out due to the need of reactive control and precise planning. The role of inter-vehicle coordination in efficient merging is also discussed in detail. While coordination of human-driven vehicles is mostly reactive, CAVs can be assigned goals proactively so as to optimize the merging process. Similarly, a detailed discussion is provided on the car overtake problem (see Section 4) to emphasize the need for explicit modelling of human behavior when designing algorithms for CAVs. This seemingly simple problem is specifically chosen to draw attention to the complexities that could arise due to the presence of human drivers on the road. A naive data-driven algorithm can fail catastrophically in scenarios where humans may behave unpredictably so an overview of algorithms that explicitly take human behavior into consideration is provided later on in this chapter.

*Models and Methods for Intelligent Highway Routing of Human-Driven and Connected… DOI: http://dx.doi.org/10.5772/intechopen.94332*
