Mobile Ad Hoc Networking (Cutting Edge Directions) || Mobility Models, Topology, and Simulations in VANET

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<ul><li><p>15MOBILITY MODELS, TOPOLOGY, ANDSIMULATIONS IN VANET</p><p>Francisco J. Ros, Juan A. Martinez, and Pedro M. Ruiz</p><p>ABSTRACT</p><p>Vehicular networks are likely to be the very first deployed large-scale instance ofmobile ad hoc networks. The design of reliable and adaptive protocols in vehicu-lar context is challenging, especially due to the high dynamicity of the underlyingtopology and its intermittent connectivity in most scenarios. Yet, the movement ofcars is constrained by the road structure, and this fact can be exploited to improvenetworking tasks. It is also expected that a partial infrastructure is still to be availableat some strategic places (e.g., at intersections inside cities) to improve the connecti-vity and provide dedicated services to drivers and passengers. This chapter discussessome aspects related to the modeling of roads and traffic. In particular, it reviews dif-ferent models and tools for the realistic simulation of vehicular networks, includingcurrent simulators employed in VANET research. Moreover, a connectivity analysisin a highway scenario is conducted.</p><p>15.1 INTRODUCTION AND MOTIVATION</p><p>Vehicular ad hoc networks (VANETs) have attracted the interest of most relevantplayers in the development of future Intelligent Transportation Systems (ITS). In fact,many of the envisioned services for the vehicular environment rely on the provision</p><p>Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition. Edited by Stefano Basagni,Marco Conti, Silvia Giordano, and Ivan Stojmenovic. 2013 by The Institute of Electrical and Electronics Engineers, Inc. Published 2013 by John Wiley &amp; Sons, Inc.</p><p>545</p></li><li><p>546 MOBILITY MODELS, TOPOLOGY, AND SIMULATIONS IN VANET</p><p>of an effective communication platform among the vehicles themselves. Internationalstandardization bodies are pushing technical specifications for vehicular ad hoc com-munications, which are expected to be adopted by the industry. In such scenario,VANETs are likely to be the first real large-scale deployment of a mobile ad hoc net-work. VANETs offer a great number of possibilities for the development of vehicularservices. For instance, a safety service in a car that has been involved in an accidentcan take advantage of the ad hoc network to communicate this dangerous situationto nearby vehicles. In a similar way, a traffic management application can announcethat a given road is congested, so that incoming vehicles could take an alternativeroute. Many other services are to be developed by exploiting the ad hoc networkingparadigm. Among them, we can highlight location-based applications like toll-payservices and advertisement of petrol station prices.</p><p>In order to develop effective vehicular services, the particular characteristics ofthis mobile environment must be well understood. Luckily, this subject has beeninvestigated for a long time by companies and institutions interested in buildingefficient roads and highways, with the objective of improving driving quality andreducing traffic congestion. Such studies originated the development of different toolsrelated to traffic mobility.</p><p>Mobility models are one of these tools. Many of them are based on the idea of a carfollowing another and how it behaves depending on the distance to the leading one.In Section 15.2 we review some of the most relevant car following and multi-lanetraffic models.</p><p>We also review some mobility simulators in Section 15.3. They allow for thesimulation of vehicles moving throughout a given scenario. Hence, researches canevaluate the impact that new roads would have in a specific area, or gather relevantinformation on traffic density, average speed of the vehicles, occupancy degree ofeach lane, and the like.</p><p>Despite these powerful mobility simulators, VANET researchers are interested inobtaining results within the communication technology context. Many well-knownnetwork simulators (for instance, NS-2 or OMNet++, just to name a few) are ableto receive as input a trace file that describes the movement of a set of nodes along aperiod of time. However, the simulation of vehicular services has revealed new needsto be covered by the simulation tools. In particular, communications and vehiclemovements are not independent any more, since the former can influence the latter.For instance, when a crash occurs, a safety application can trigger the disseminationof a broadcast message that is received by nearby vehicles. When processing this typeof message, these vehicles must reduce their current speed (coming to a standstill ifneeded), preventing them from being involved in the accident. Traditional mobilitysimulators are not able to deal with these scenarios, since there is no coupling betweenthe communication and mobility simulators. Therefore, integrated simulators thataccount for these types of coupled behavior have been developed. Section 15.4 reviewssome of the most relevant ones.</p><p>Simulation is a very useful tool for the design of communication-based vehicularservices and protocols. However, the simulation model must be properly configuredin order to produce realistic results. One of the key parameters that must be taken</p></li><li><p>MOBILITY MODELS 547</p><p>into account when modeling a vehicular scenario is how the radio signal behaves insuch an environment. It is well known that simplistic assumptions in this regard canlead to wrong conclusions. Therefore, simulators for VANET research must includerealistic wireless signal propagation models, like the ones reviewed in Section 15.5.</p><p>In addition to fine-tuned signal propagation models, VANET simulations mustaccount for realistic movement patterns. Vehicles constrain their movement to the roadlayout, traffic signals, and other vehicles movement, among others. These featuresmake the network topology very dynamic, causing frequent network partitions andjoins. Section 15.6 studies the connectivity level that can be expected in a highway,as a function of the effective radio range that can be obtained with VANET interfacecards.</p><p>Finally, Section 15.7 concludes this chapter. We summarize the main features ofthe vehicular networking paradigm and provide some hints on how to deal with thesimulation of this kind of scenario.</p><p>15.2 MOBILITY MODELS</p><p>In general, vehicular mobility models can be classified into microscopic, macroscopic,and mesoscopic models. Macroscopic models are aimed at dealing with traffic density,traffic flows, and initial vehicle distribution modeling. On the other hand, microscopicmodels are in charge of modeling the location, velocity, and acceleration of each ve-hicle that participates in the simulated scenario. Finally, as an intermediate approach,mesoscopic models aggregate the movements of different nodes.</p><p>In this chapter we focus on the behavior of each vehicle as an independent unit.Therefore, we constrain our review to microscopic models, namely car following andmulti-lane traffic models.</p><p>15.2.1 Car Following</p><p>One of the most studied tasks involved in driving is that of a vehicle following thevehicle ahead along a lane of the roadway. Car following is simpler than other facetsof driving, and it has a great impact onto the macroscopic characteristics of trafficflow. Therefore, this topic has been the focus of deep study for several decades.</p><p>According to the literature [1,2], car-following models can be classified in thefollowing groups:</p><p> Stimulus-Response Models. Chandler model (1958), generalized GM model(1961).</p><p> Safe Distance Models. Gipps model (1981), Krauss model (1997). Psychophysical Models. Leutzbach model (1986). Cell-Based Models. cellular automata model (Nagel (1992)). Optimum Velocity Models. Bando et al. (1995). Trajectory-Based Models. Newell model (2002).</p></li><li><p>548 MOBILITY MODELS, TOPOLOGY, AND SIMULATIONS IN VANET</p><p>Let us briefly summarize these approaches and highlight their key aspects:</p><p> StimulusResponse Model. It is assumed that the reaction of a driver isproportional to the stimulus he perceives. Following this statement, Chandler proposeda simple model [3] in which the relative speed with respect to the leading vehicle is theonly stimulus that the driver receives. The corresponding response takes place after agiven response time T . Since not every driver reacts at the same time given the samestimulus, the Chandler model also introduces a sensitivity factor . General Motorsconducted additional research on the subject, introducing new parameters into themodel such as the speed of the vehicles and the distance between them. This moregeneral model is commonly referred to as the GM model [4].</p><p> Safe Distance Model. One of the most important recommendations whichis followed by a good driver, consists of choosing the speed according to a safe distancewith the leading vehicle (in order to prevent a possible collision). This idea was firstintroduced into a car following model by Kometani and Sasaki [5]. Gipps extendedthe former model by making some common-sense assumptions about acceleration,deceleration, or maximum speeds, among others [6]. Later on, Krauss [7] proposeda variant of the Gipps model by introducing a stochastic term.</p><p> Psychophysical Model. This model considers the acceleration of thevehicle ahead as a stimulus for the following vehicle. It also considers the differ-ence between the current spacing and the desired following distance. This model wasproposed in Leutzbach and Wiedemann [8].</p><p> Cell-Based Model. This model, also known as cellular automata, was in-troduced by Nagel and Schreckenberg [9]. It considers two parameters to be opti-mized: the acceleration and the desired maximum speed. The particularity of thismodel is that it divides the traffic scenario into a set of cells of equal size. The sizeof the cells normally do not exceed the size of a vehicle, therefore only one vehiclewill be in each cell at a time. This model can be seen as a set of rules that control themovement of a vehicle from a cell to the next one.</p><p> Optimum Velocity Model. The first proposal based on the optimum ve-locity concept was presented in [10]. Within this model, the optimum velocity isthe required speed to maintain a given distance with the vehicle ahead. Thus, at anytime, the response of the following driver is proportional to the difference betweenhis optimum speed and his current speed.</p><p> Trajectory-Based Model. Finally, Newell [11] introduced a new modelthat takes into account the trajectory of the leading vehicle. It is assumed that thetrajectory of the leading vehicle and that of the following one are the same, exceptfor a translation in space and time. Thus, the following vehicle drives as a shiftedtrajectory of the vehicle ahead.</p></li><li><p>MOBILITY MODELS 549</p><p>Figure 15.1 Nearest neighbors of vehicle c considering lane change to the left (new andold successor are denoted by n and o respectively; acceleration after possible lane change isdenoted with a tilde).</p><p>15.2.2 Multi-lane Traffic</p><p> MOBIL. Single-lane car-following models were the first step to modelthe traffic of an entire road. However, real traffic flows consist of different types ofvehicles (cars, trucks, motorbikes, buses, and so on) traveling along several lanesat different speeds, thus generating heterogeneous traffic streams along roads. Thatis the reason why realistic traffic can only be simulated by including a multi-lanemodeling framework, in order to let faster vehicles overtake slower ones. In addition,traffic safety is directly affected by the lane-changing behavior of the drivers.</p><p>The following strategy for modeling lane changes consists of minimizing the over-all braking induced by the lane change (MOBIL) [12]. In Figure 15.1, vehicle c con-siders changing to the left lane. The decision generally depends on the vehicles inthe current lane (vehicle o) and in the target lane (vehicle n). Furthermore, within thelane-changing criteria, two main aspects are often differentiated. On one hand, themodel must provide an incentive for the vehicle to change its current lane. On theother hand, there are some safety restrictions that must be accomplished in order tomake a safe lane change.</p><p>Therefore, the safety criterion guarantees that, after a lane change, the decelera-tion of the successor (vehicle n) does not exceed a given safe limit. The former isrepresented in equation (15.1), where an is the acceleration of a vehicle after the lanechange, and bsafe is the limit for a safe deceleration.</p><p>an &gt;= bsafe (15.1)Single-lane car-following models are aware of the difference of speed between</p><p>vehicles. This dependence is also transferred to the lane-changing decisions. Thus,larger gaps between the new follower vehicle in the target lane (vehicle n) and theown position are required if the follower is faster than the own vehicle. On the otherhand, lower values for the gap are allowed if the speed of the following vehicle islower.</p><p>Safe-braking decelerations are modeled in longitudinal car-following models,therefore crashes due to lane changes are automatically excluded. The maximumpossible deceleration (bmax) is about 9 m/s2 on dry surfaces. Hence, depending onthe bsafe value employed in simulations, accidents are prevented even in the case ofselfish drivers (bsafe &lt; bmax), whereas higher values than bsafe will provoke strongerperturbations due to individual lane changes.</p></li><li><p>550 MOBILITY MODELS, TOPOLOGY, AND SIMULATIONS IN VANET</p><p>The incentive criterion determines if the individual traffic situation of a driver isimproved by a lane change. This model also extends this evaluation to the immediatelyaffected neighbors as well. Thus, the incentive condition for making a lane-changingdecision for symmetric overtaking rules is given by equation (15.2).</p><p>ac ac driver</p><p>+p( an an new follower</p><p>+ ao ao old follower</p><p>) &gt; ath (15.2)</p><p>Taking a look at the expression, the first two terms denote the improvement, interms of traffic conditions, of a possible lane change for the driver c (that is, thedifference between the new acceleration that the vehicle will have in the new laneand the acceleration it has in the current one). The third term of the equation givesthe same advantage of the two neighbors weighted by the politeness factor p. Finally,the switching threshold ath on the right-hand side of the equation models a certainresistance to make the decision of lane changing that is identified by the keep lanedirective. In fact, this equation also contains a safety restriction for the lane-changingvehicle. Lanes are only changed if the deceleration in the target lane is lower than inthe current one weighted by the politeness factor. It is worth noting that whereas thethreshold ath models the overall vehicle behavior, the politeness factor only affectsthe local lane-changing behavior depending on the involved neighbors.</p><p>Although symmetric lane-changing rules can be applied in many highways, inmost European countries the driving rules also legislate the lane usage. For instance,in Spain, left lanes must be used only for overtaking other slower vehicles, and whilethere is no...</p></li></ul>